Conference segmentation based on conversational dynamics

ABSTRACT

Various disclosed implementations involve processing and/or playback of a recording of a conference involving a plurality of conference participants. Some implementations disclosed herein involve analyzing conversational dynamics of the conference recording. Some examples may involve searching the conference recording to determine instances of segment classifications. The segment classifications may be based, at least in part, on conversational dynamics data. Some implementations may involve segmenting the conference recording into a plurality of segments, each of the segments corresponding with a time interval and at least one of the segment classifications. Some implementations allow a listener to scan through a conference recording quickly according to segments, words, topics and/or talkers of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to International PatentApplication No. PCT/CN2015/072168 filed 3 Feb. 2015; and U.S.Provisional Patent Application No. 62/170,236 filed 3 Jun. 2015, thecontents of which are hereby incorporated by reference.

TECHNICAL FIELD

This disclosure relates to the processing of audio signals. Inparticular, this disclosure relates to processing audio signals relatedto conferencing, including but not limited to processing audio signalsfor teleconferencing or video conferencing.

BACKGROUND

In the field of teleconferencing, it is customary to provide a facilityto allow the recording of the teleconference for playback after theteleconference has finished. This can allow those who were unable toattend to hear what happened in the conference. It can also allow thosewho were present to refresh their memory of what occurred during theteleconference. Recording facilities are sometimes used to ensureregulatory compliance in some industries, such as banking.

A typical teleconference recording is a single monophonic streamcontaining a mix of all parties onto a recording medium. This is oftenimplemented by connecting a “dummy” client or phone to theteleconferencing bridge or server which appears to the bridge to be anordinary client or phone but which, in reality, may be a machine whichsimply records its downlink. In such a system, the experience oflistening to playback of the recording is identical, or substantiallyidentical, to the experience of listening passively on a phone or clientduring the original teleconference.

SUMMARY

According to some implementations disclosed herein, a method may involveprocessing audio data. Some such methods may involve receiving audiodata corresponding to a recording of a conference involving a pluralityof conference participants. In some examples, the conference may be ateleconference. However, in some examples the conference may be anin-person conference.

According to some examples, the audio data may include audio data frommultiple endpoints. The audio data for each of the multiple endpointsmay have been recorded separately. Alternatively, or additionally, atleast some of the audio data may be from a single endpoint correspondingto multiple conference participants. The audio data may include spatialinformation for each conference participant of the multiple conferenceparticipants.

In some implementations, the method may involve analyzing the audio datato determine conversational dynamics data. In some examples, theconversational dynamics data may include data indicating the frequencyand duration of conference participant speech, data indicating instancesof conference participant doubletalk during which at least twoconference participants are speaking simultaneously and/or dataindicating instances of conference participant conversations.

Some disclosed methods may involve applying the conversational dynamicsdata as one or more variables of a spatial optimization cost function ofa vector describing a virtual conference participant position for eachof the conference participants in a virtual acoustic space. Some suchmethods may involve applying an optimization technique to the spatialoptimization cost function to determine a locally optimal solution andassigning the virtual conference participant positions in the virtualacoustic space based, at least in part, on the locally optimal solution.

In some implementations, the virtual acoustic space may be determinedrelative to a position of a virtual listener's head in the virtualacoustic space. According to some such implementations, the spatialoptimization cost function may apply a penalty for placing conferenceparticipants who are involved in conference participant doubletalk atvirtual conference participant positions that are on, or within apredetermined angular distance from, a “cone of confusion” definedrelative to the position of the virtual listener's head. Circularconical slices through the cone of confusion may have identicalinter-aural time differences. In some examples, the spatial optimizationcost function may apply a penalty for placing conference participantswho are involved in a conference participant conversation with oneanother at virtual conference participant positions that are on, orwithin a predetermined angular distance from, a cone of confusion.

According to some examples, analyzing the audio data may involvedetermining which conference participants, if any, have perceptuallysimilar voices. In some such examples, the spatial optimization costfunction may apply a penalty for placing conference participants withperceptually similar voices at virtual conference participant positionsthat are on, or within a predetermined angular distance from, a cone ofconfusion.

In some examples, the spatial optimization cost function may apply apenalty for placing conference participants who speak frequently atvirtual conference participant positions that are beside, behind, above,or below the position of the virtual listener's head. In some instances,the spatial optimization cost function may apply a penalty for placingconference participants who speak frequently at virtual conferenceparticipant positions that are farther from the position of the virtuallistener's head than the virtual conference participant positions ofconference participants who speak less frequently. In someimplementations, the spatial optimization cost function may apply apenalty for placing conference participants who speak infrequently atvirtual conference participant positions that are not beside, behind,above or below the position of the virtual listener's head.

According to some examples, the optimization technique may involve agradient descent technique, conjugate gradient technique, Newton'smethod, the Broyden-Fletcher-Goldfarb-Shanno algorithm; a geneticalgorithm, an algorithm for simulated annealing, an ant colonyoptimization method and/or a Monte Carlo method. In some examples,assigning a virtual conference participant position may involveselecting a virtual conference participant position from a set ofpredetermined virtual conference participant positions.

In some instances, the audio data may include output of a voice activitydetection process. According to some examples, analyzing the audio datamay involve identifying speech corresponding to individual conferenceparticipants.

In some examples, the audio data may correspond to a recording of acomplete or substantially complete conference. Some examples may involvereceiving and processing audio data from more than one conference.

Some disclosed methods may involve receiving (e.g., via an interfacesystem) teleconference audio data during a teleconference. In someexamples, the teleconference audio data may include a plurality ofindividual uplink data packet stream. Each uplink data packet stream maycorrespond to a telephone endpoint used by one or more teleconferenceparticipants. The method may involve sending (e.g., via the interfacesystem) the teleconference audio data to a memory system as individualuplink data packet streams.

Some methods may involve determining that a late data packet of anincomplete uplink data packet stream has been received from a telephoneendpoint after a late packet time threshold. The late packet timethreshold may be greater than or equal to a mouth-to-ear latency timethreshold of the teleconference. In some examples, the mouth-to-earlatency time threshold may be greater than or equal to 100 milliseconds(ms). In some instances, the mouth-to-ear latency time threshold may be150 ms or less. In some examples, the late packet time threshold may be200 ms, 400 ms, 500 ms or more. In some implementations, the late packettime threshold may be greater than or equal to 1 second. Some suchmethods may involve adding the late data packet to the incomplete uplinkdata packet stream.

Some methods may involve determining that a missing data packet of anincomplete uplink data packet stream has not been received from atelephone endpoint within a missing packet time threshold that isgreater than the late packet time threshold. Some such methods mayinvolve transmitting a request to the telephone endpoint (e.g., via theinterface system) to re-send the missing data packet. If the telephoneendpoint re-sends the missing data packet, such methods may involvereceiving the missing data packet and adding the missing data packet tothe incomplete uplink data packet stream.

In some examples, the individual uplink data packet streams may beindividual encoded uplink data packet streams. At least one of theuplink data packet streams may include at least one data packet that wasreceived after a mouth-to-ear latency time threshold of theteleconference and was therefore not used for reproducing audio dataduring the teleconference. In some instances, at least one of the uplinkdata packet streams may correspond to multiple teleconferenceparticipants and may include spatial information regarding each of themultiple participants.

Some disclosed methods may involve receiving (e.g., via an interfacesystem) recorded audio data for a teleconference. The recorded audiodata may include an individual uplink data packet stream correspondingto a telephone endpoint used by one or more teleconference participants.Some such methods may involve analyzing sequence number data of datapackets in the individual uplink data packet stream. The analyzingprocess may involve determining whether the individual uplink datapacket stream includes at least one out-of-order data packet. Suchmethods may involve re-ordering the individual uplink data packet streamaccording to the sequence number data if the uplink data packet streamincludes at least one out-of-order data packet. In some instances, atleast one data packet of the individual uplink data packet stream mayhave been received after a mouth-to-ear latency time threshold of theteleconference.

Some such methods may involve receiving (e.g., via the interface system)teleconference metadata and indexing the individual uplink data packetstream based, at least in part, on the teleconference metadata. In someinstances, the recorded audio data may include a plurality of individualencoded uplink data packet streams. Each of the individual encodeduplink data packet streams may correspond to a telephone endpoint usedby one or more teleconference participants. Such methods may involvedecoding the plurality of individual encoded uplink data packet streamsand analyzing the plurality of individual uplink data packet streams.

Some methods may involve recognizing speech in one or more individualdecoded uplink data packet streams and generating speech recognitionresults data. Some such methods may involve identifying keywords in thespeech recognition results data and indexing keyword locations.

Some disclosed methods may involve identifying speech of each ofmultiple teleconference participants in an individual decoded uplinkdata packet stream. Some such methods may involve generating a speakerdiary indicating times at which each of the multiple teleconferenceparticipants were speaking.

According to some examples, analyzing the plurality of individual uplinkdata packet streams may involve determining conversational dynamicsdata. The conversational dynamics data may include data indicating thefrequency and duration of conference participant speech, data indicatinginstances of conference participant doubletalk during which at least twoconference participants are speaking simultaneously and/or dataindicating instances of conference participant conversations.

Some methods may involve receiving audio data corresponding to arecording of a conference involving a plurality of conferenceparticipants. In some examples, the conference may be a teleconference.However, in some examples the conference may be an in-person conference.

According to some examples, the audio data may include audio data frommultiple endpoints. The audio data for each of the multiple endpointsmay have been recorded separately. Alternatively, or additionally, atleast some of the audio data may be from a single endpoint correspondingto multiple conference participants. The audio data may include spatialinformation for each conference participant of the multiple conferenceparticipants.

Some such methods may involve rendering the conference participantspeech data in a virtual acoustic space such that each of the conferenceparticipants has a respective different virtual conference participantposition. Such methods may involve scheduling the conference participantspeech for playback such that an amount of playback overlap between atleast two output talkspurts of the conference participant speech isdifferent from (e.g., greater than) an amount of original overlapbetween two corresponding input talkspurts of the conference recording.The amount of original overlap may be zero or non-zero.

In some examples, the scheduling may be performed, at least in part,according to a set of perceptually-motivated rules. Various types ofperceptually-motivated rules are disclosed herein. In someimplementations, the set of perceptually-motivated rules may include arule indicating that two output talkspurts of a single conferenceparticipant should not overlap in time. The set ofperceptually-motivated rules may include a rule indicating that twooutput talkspurts should not overlap in time if the two outputtalkspurts correspond to a single endpoint.

According to some implementations, given two consecutive inputtalkspurts A and B, A having occurred before B, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started. The setof perceptually-motivated rules may include a rule allowing the playbackof an output talkspurt corresponding to B to begin no sooner than a timeT before the playback of an output talkspurt corresponding to A iscomplete. In some such examples, T may be greater than zero.

According to some implementations, the set of perceptually-motivatedrules may include a rule allowing the concurrent playback of entirepresentations from different conference participants. In someimplementations, a presentation may correspond with a time interval ofthe conference participant speech during which a speech density metricis greater than or equal to a silence threshold, a doubletalk ratio isless than or equal to a discussion threshold and a dominance metric isgreater than a presentation threshold. The doubletalk ratio may indicatea fraction of speech time in the time interval during which at least twoconference participants are speaking simultaneously. The speech densitymetric may indicate a fraction of the time interval during which thereis any conference participant speech. The dominance metric may indicatea fraction of total speech uttered by a dominant conference participantduring the time interval. The dominant conference participant may be aconference participant who spoke the most during the time interval.

In some examples, at least some of the conference participant speech maybe scheduled to be played back at a faster rate than the rate at whichthe conference participant speech was recoded. According to some suchexamples, scheduling the playback of the speech at the faster rate maybe accomplished by using a WSOLA (Waveform Similarity Based Overlap Add)technique.

Some disclosed methods may involve analyzing the audio data to determineconversational dynamics data. The conversational dynamics data mayinclude data indicating the frequency and duration of conferenceparticipant speech, data indicating instances of conference participantdoubletalk during which at least two conference participants arespeaking simultaneously and/or data indicating instances of conferenceparticipant conversations. Some such methods may involve applying theconversational dynamics data as one or more variables of a spatialoptimization cost function of a vector describing the virtual conferenceparticipant position for each of the conference participants in thevirtual acoustic space. Such methods may involve applying anoptimization technique to the spatial optimization cost function todetermine a locally optimal solution and assigning the virtualconference participant positions in the virtual acoustic space based, atleast in part, on the locally optimal solution.

In some examples, the audio data may include output of a voice activitydetection process. Some implementations may involve identifying speechcorresponding to individual conference participants. In someimplementations, the audio data corresponds to a recording of at leastone complete or substantially complete conference.

Some methods may involve receiving (e.g., by a conversational dynamicsanalysis module) audio data corresponding to a recording of a conferenceinvolving a plurality of conference participants. In some examples, theconference may be a teleconference. However, in some examples theconference may be an in-person conference.

According to some examples, the audio data may include audio data frommultiple endpoints. The audio data for each of the multiple endpointsmay have been recorded separately. Alternatively, or additionally, atleast some of the audio data may be from a single endpoint correspondingto multiple conference participants. The audio data may includeinformation for identifying conference participant speech for eachconference participant of the multiple conference participants.

Some such methods may involve analyzing conversational dynamics of theconference recording to determine conversational dynamics data. Somemethods may involve searching the conference recording to determineinstances of each of a plurality of segment classifications. Each of thesegment classifications may be based, at least in part, on theconversational dynamics data. Some implementations may involvesegmenting the conference recording into a plurality of segments. Eachof the segments may correspond with a time interval and at least one ofthe segment classifications.

In some examples, the analyzing, searching and segmenting processes maybe performed by the conversational dynamics analysis module. Thesearching and segmenting processes may, in some implementations, berecursive processes. In some implementations, the searching andsegmenting processes may be performed multiple times at different timescales.

According to some implementations, the searching and segmentingprocesses may be based, at least in part, on a hierarchy of segmentclassifications. In some examples, the hierarchy of segmentclassifications may be based a level of confidence with which segmentsof a particular segment classification may be identified, a level ofconfidence with which a start time of a segment may be determined, alevel of confidence with which an end time of a segment may bedetermined and/or a likelihood that a particular segment classificationincludes conference participant speech corresponding to a conferencetopic.

In some implementations, instances of the segment classifications may bedetermined according to a set of rules. The rules may, for example, bebased on one or more conversational dynamics data types such as adoubletalk ratio indicating a fraction of speech time in a time intervalduring which at least two conference participants are speakingsimultaneously, a speech density metric indicating a fraction of thetime interval during which there is any conference participant speechand/or a dominance metric indicating a fraction of total speech utteredby a dominant conference participant during the time interval. Thedominant conference participant may be a conference participant whospoke the most during the time interval.

In some examples, the set of rules may include a rule that classifies asegment as a Mutual Silence segment if the speech density metric is lessthan a mutual silence threshold. According to some examples, the set ofrules may include a rule that classifies a segment as a Babble segmentif the speech density metric is greater than or equal to the mutualsilence threshold and the doubletalk ratio is greater than a babblethreshold. In some implementations, the set of rules may include a rulethat classifies a segment as a Discussion segment if the speech densitymetric is greater than or equal to the silence threshold and if thedoubletalk ratio is less than or equal to the babble threshold butgreater than a discussion threshold.

According to some implementations, the set of rules may include a rulethat classifies a segment as a Presentation segment if the speechdensity metric is greater than or equal to the silence threshold, if thedoubletalk ratio is less than or equal to the discussion threshold andif the dominance metric is greater than a presentation threshold. Insome examples, the set of rules may include a rule that classifies asegment as a Question and Answer segment if the speech density metric isgreater than or equal to the silence threshold, if the doubletalk ratiois less than or equal to the discussion threshold and if the dominancemetric is less than or equal to the presentation threshold but greaterthan a question and answer threshold.

As noted above, in some implementations the searching and segmentingprocesses may be based, at least in part, on a hierarchy of segmentclassifications. According to some such implementations, a firsthierarchical level of the searching process may involve searching theconference recording to determine instances of Babble segments. In someexamples, a second hierarchical level of the searching process mayinvolve searching the conference recording to determine instances ofPresentation segments.

According to some examples, a third hierarchical level of the searchingprocess may involve searching the conference recording to determineinstances of Question and Answer segments. According to someimplementations, a fourth hierarchical level of the searching processmay involve searching the conference recording to determine instances ofDiscussion segments.

However, in some alternative implementations, instance of the segmentclassifications may be determined according to a machine learningclassifier. In some examples, the machine learning classifier may be anadaptive boosting technique, a support vector machine technique, aBayesian network model technique, a neural networks technique, a hiddenMarkov model technique or a conditional random fields technique.

Some disclosed methods may involve receiving (e.g., by a topic analysismodule) speech recognition results data for at least a portion of arecording of a conference involving a plurality of conferenceparticipants. The speech recognition results data may include aplurality of speech recognition lattices and a word recognitionconfidence score for each of a plurality of hypothesized words of thespeech recognition lattices. The word recognition confidence score maycorrespond with a likelihood of a hypothesized word correctlycorresponding with an actual word spoken by a conference participantduring the conference. In some examples, receiving the speechrecognition results data may involve receiving speech recognitionresults data from two or more automatic speech recognition processes.

Some such methods may involve determining a primary word candidate andone or more alternative word hypotheses for each of a plurality ofhypothesized words in the speech recognition lattices. The primary wordcandidate may have a word recognition confidence score indicating ahigher likelihood of correctly corresponding with the actual word spokenby the conference participant during the conference than a wordrecognition confidence score of any of the one or more alternative wordhypotheses.

Some methods may involve calculating a term frequency metric of theprimary word candidates and the alternative word hypotheses. The termfrequency metric may be based, at least in part, on a number ofoccurrences of a hypothesized word in the speech recognition latticesand the word recognition confidence score. According to someimplementations, calculating the term frequency metric may be based, atleast in part, on a number of word meanings. Some such methods mayinvolve sorting the primary word candidates and alternative wordhypotheses according to the term frequency metric, including thealternative word hypotheses in an alternative hypothesis list andre-scoring at least some hypothesized words of the speech recognitionlattices according to the alternative hypothesis list.

Some implementations may involve forming a word list. The word list mayinclude primary word candidates and a term frequency metric for each ofthe primary word candidates. In some examples, the term frequency metricmay be inversely proportional to a document frequency metric. Thedocument frequency metric may correspond to an expected frequency withwhich a primary word candidate will occur in the conference. Accordingto some examples, the expected frequency may correspond to a frequencywith which the primary word candidate has occurred in two or more priorconferences or a frequency with which the primary word candidate occursin a language model.

According to some examples, the word list also may include one or morealternative word hypotheses for each primary word candidate. In someinstances, alternative word hypotheses may be generated according tomultiple language models.

Some methods may involve generating a topic list of conference topicsbased, at least in part, on the word list. In some examples, generatingthe topic list may involve determining a hypernym of at least one wordof the word list. According to some such examples, generating the topiclist may involve determining a topic score. In some examples, the topicscore may include a hypernym score. According to some such examples, theincluding process may involve including alternative word hypotheses inthe alternative hypothesis list based, at least in part, on the topicscore.

In some implementations, two or more iterations of at least thedetermining, calculating, sorting, including and re-scoring processesmay be performed. According to some examples, the iterations may involvegenerating the topic list and determining the topic score. In someexamples, the alternative hypothesis list may be retained after eachiteration.

Some implementations may involve reducing at least some hypothesizedwords of a speech recognition lattice to a canonical base form. Forexample, the reducing may involve reducing nouns of the speechrecognition lattice to the canonical base form. The canonical base formmay be a singular form of a noun. Alternatively, or additionally, thereducing may involve reducing verbs of the speech recognition lattice tothe canonical base form. The canonical base form may be an infinitiveform of a verb.

According to some examples, the conference recording may includeconference participant speech data from multiple endpoints, recordedseparately. Alternatively, or additionally, the conference recording mayinclude conference participant speech data from a single endpointcorresponding to multiple conference participants, which may includeinformation for identifying conference participant speech for eachconference participant of the multiple conference participants.

Some disclosed methods may involve receiving audio data corresponding toa recording of at least one conference involving a plurality ofconference participants. The audio data may include conferenceparticipant speech data from multiple endpoints, recorded separatelyand/or conference participant speech data from a single endpointcorresponding to multiple conference participants, which may includespatial information for each conference participant of the multipleconference participants.

Such methods may involve determining search results based on a search ofthe audio data. The search may be, or may have been, based on one ormore search parameters. The search results may correspond to at leasttwo instances of conference participant speech in the audio data. Theinstances of conference participant speech may, for example, includetalkspurts and/or portions of talkspurts. The instances of conferenceparticipant speech may include a first instance of speech uttered by afirst conference participant and a second instance of speech uttered bya second conference participant.

Some such methods may involve rendering the instances of conferenceparticipant speech to at least two different virtual conferenceparticipant positions of a virtual acoustic space, such that the firstinstance of speech is rendered to a first virtual conference participantposition and the second instance of speech is rendered to a secondvirtual conference participant position. Such methods may involvescheduling at least a portion of the instances of conference participantspeech for simultaneous playback, to produce playback audio data.

According to some implementations, determining the search results mayinvolve receiving search results. For example, determining the searchresults may involve receiving the search results resulting from a searchperformed by another device, e.g., by a server.

However, in some implementations determining the search results mayinvolve performing a search. According to some examples, determining thesearch results may involve performing a concurrent search of the audiodata regarding multiple features. According to some implementations, themultiple features may include two or more features selected from a setof features. The set of features may include words, conference segments,time, conference participant emotion, endpoint location and/or endpointtype. In some implementations, determining the search results mayinvolve performing a search of audio data that corresponds to recordingsof multiple conferences. In some examples, the scheduling process mayinvolve scheduling the instances of conference participant speech forplayback based, at least in part, on a search relevance metric.

Some implementations may involve modifying a start time or an end timeof at least one of the instances of conference participant speech. Insome examples, the modifying process may involve expanding a timeinterval corresponding to an instance of conference participant speech.According to some examples, the modifying process may involve mergingtwo or more instances of conference participant speech, correspondingwith a single conference endpoint, that overlap in time after theexpanding.

In some examples, the scheduling process may involve scheduling aninstance of conference participant speech that did not previouslyoverlap in time to be played back overlapped in time. Alternatively, oradditionally, some methods may involve scheduling an instance ofconference participant speech that was previously overlapped in time tobe played back further overlapped in time.

According to some implementations, the scheduling may be performedaccording to a set of perceptually-motivated rules. In someimplementations, the set of perceptually-motivated rules may include arule indicating that two output talkspurts of a single conferenceparticipant should not overlap in time. The set ofperceptually-motivated rules may include a rule indicating that twooutput talkspurts should not overlap in time if the two outputtalkspurts correspond to a single endpoint.

According to some implementations, given two consecutive inputtalkspurts A and B, A having occurred before B, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started. The setof perceptually-motivated rules may include a rule allowing the playbackof an output talkspurt corresponding to B to begin no sooner than a timeT before the playback of an output talkspurt corresponding to A iscomplete. In some such examples, T may be greater than zero.

Some disclosed methods may involve analyzing the audio data to determineconversational dynamics data. The conversational dynamics data mayinclude data indicating the frequency and duration of conferenceparticipant speech, data indicating instances of conference participantdoubletalk during which at least two conference participants arespeaking simultaneously and/or data indicating instances of conferenceparticipant conversations. Some such methods may involve applying theconversational dynamics data as one or more variables of a spatialoptimization cost function of a vector describing the virtual conferenceparticipant position for each of the conference participants in thevirtual acoustic space. Such methods may involve applying anoptimization technique to the spatial optimization cost function todetermine a locally optimal solution and assigning the virtualconference participant positions in the virtual acoustic space based, atleast in part, on the locally optimal solution.

Some implementations may involve providing instructions for controllinga display to provide a graphical user interface. According to someimplementations, the instructions for controlling the display mayinclude instructions for making a presentation of conferenceparticipants. The one or more features for performing the search may,for example, include an indication of a conference participant.

In some examples, the instructions for controlling the display mayinclude instructions for making a presentation of conference segments.The one or more features for performing the search may, for example,include an indication of a conference segment.

In some instances, the instructions for controlling the display mayinclude instructions for making a presentation of a display area forsearch features. The one or more features for performing the search may,for example, include words, time, conference participant emotion,endpoint location and/or endpoint type.

Some such implementations may involve receiving input corresponding to auser's interaction with the graphical user interface and processing theaudio data based, at least in part, on the input. In some examples, theinput may correspond to one or more features for performing a search ofthe audio data. Some such methods may involve providing the playbackaudio data to a speaker system.

According to some implementations, determining the search results mayinvolve searching a keyword spotting index. In some examples, thekeyword spotting index may have a data structure that includes pointersto contextual information. According to some such examples, the pointersmay be, or may include, vector quantization indices.

In some examples, determining the search results may involve a firststage of determining one or more conference(s) for searching, e.g.,according to one or more time parameters. Some such methods may involvea second stage of retrieving search results according to other searchparameters.

Some disclosed methods may involve receiving audio data corresponding toa recording of a conference. The audio data may include datacorresponding to conference participant speech of each of a plurality ofconference participants. Such methods may involve selecting only aportion of the conference participant speech as playback audio data.

According to some implementations, the selecting process may involve atopic selection process of selecting conference participant speech forplayback according to estimated relevance of the conference participantspeech to one or more conference topics. In some implementations, theselecting process may involve a topic selection process of selectingconference participant speech for playback according to estimatedrelevance of the conference participant speech to one or more topics ofa conference segment.

In some instances, the selecting process may involve removing inputtalkspurts having an input talkspurt time duration that is below athreshold input talkspurt time duration. According to some examples, theselecting process may involve a talkspurt filtering process of removinga portion of input talkspurts having an input talkspurt time durationthat is at or above the threshold input talkspurt time duration.

Alternatively, or additionally, the selecting process may involve anacoustic feature selection process of selecting conference participantspeech for playback according to at least one acoustic feature. In someexamples, the selecting may involve an iterative process. Some suchimplementations may involve providing the playback audio data to aspeaker system for playback.

Some methods may involve receiving an indication of a target playbacktime duration. According to some such examples, the selecting processmay involve making a time duration of the playback audio data within athreshold time difference and/or or a threshold time percentage of thetarget playback time duration. In some examples, the time duration ofthe playback audio data may be determined, at least in part, bymultiplying a time duration of at least one selected portion of theconference participant speech by an acceleration coefficient.

According to some examples, the audio data may include conferenceparticipant speech data from multiple endpoints, recorded separately orconference participant speech data from a single endpoint correspondingto multiple conference participants, which may include spatialinformation for each conference participant of the multiple conferenceparticipants. Some such methods may involve rendering the playback audiodata in a virtual acoustic space such that each of the conferenceparticipants whose speech is included in the playback audio data has arespective different virtual conference participant position.

According to some implementations, the selecting process may involve atopic section process. According to some such examples, the topicsection process may involve receiving a topic list of conference topicsand determining a list of selected conference topics. The list ofselected conference topics may be a subset of the conference topics.

Some methods may involve receiving topic ranking data, which mayindicate an estimated relevance of each conference topic on the topiclist. Determining the list of selected conference topics may be based,at least in part, on the topic ranking data.

According to some implementations, the selecting process may involve atalkspurt filtering process. The talkspurt filtering process may, forexample, involve removing an initial portion of an input talkspurt. Theinitial portion may be a time interval from an input talkspurt starttime to an output talkspurt start time. Some methods may involvecalculating an output talkspurt time duration based, at least in part,on an input talkspurt time duration.

Some such methods may involve determining whether the output talkspurttime duration exceeds an output talkspurt time threshold. If it isdetermined that the output talkspurt time duration exceeds an outputtalkspurt time threshold, the talkspurt filtering process may involvegenerating multiple instances of conference participant speech for asingle input talkspurt. According to some such examples, at least one ofthe multiple instances of conference participant speech may have an endtime that corresponds with an input talkspurt end time.

According to some implementations, the selecting process may involve anacoustic feature selection process. In some examples, the acousticfeature selection process may involve determining at least one acousticfeature, such as pitch variance, speech rate and/or loudness.

Some implementations may involve modifying a start time or an end timeof at least one of the instances of conference participant speech. Insome examples, the modifying process may involve expanding a timeinterval corresponding to an instance of conference participant speech.According to some examples, the modifying process may involve mergingtwo or more instances of conference participant speech, correspondingwith a single conference endpoint, that overlap in time after theexpanding.

In some examples, the scheduling process may involve scheduling aninstance of conference participant speech that did not previouslyoverlap in time to be played back overlapped in time. Alternatively, oradditionally, some methods may involve scheduling an instance ofconference participant speech that was previously overlapped in time tobe played back further overlapped in time.

According to some implementations, the scheduling may be performedaccording to a set of perceptually-motivated rules. In someimplementations, the set of perceptually-motivated rules may include arule indicating that two output talkspurts of a single conferenceparticipant should not overlap in time. The set ofperceptually-motivated rules may include a rule indicating that twooutput talkspurts should not overlap in time if the two outputtalkspurts correspond to a single endpoint.

According to some implementations, given two consecutive inputtalkspurts A and B, A having occurred before B, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started. The setof perceptually-motivated rules may include a rule allowing the playbackof an output talkspurt corresponding to B to begin no sooner than a timeT before the playback of an output talkspurt corresponding to A iscomplete. In some such examples, T may be greater than zero. Someimplementations may involve scheduling instances of conferenceparticipant speech for playback based, at least in part, on a searchrelevance metric.

Some disclosed methods may involve analyzing the audio data to determineconversational dynamics data. The conversational dynamics data mayinclude data indicating the frequency and duration of conferenceparticipant speech, data indicating instances of conference participantdoubletalk during which at least two conference participants arespeaking simultaneously and/or data indicating instances of conferenceparticipant conversations. Some such methods may involve applying theconversational dynamics data as one or more variables of a spatialoptimization cost function of a vector describing the virtual conferenceparticipant position for each of the conference participants in thevirtual acoustic space. Such methods may involve applying anoptimization technique to the spatial optimization cost function todetermine a locally optimal solution and assigning the virtualconference participant positions in the virtual acoustic space based, atleast in part, on the locally optimal solution.

Some implementations may involve providing instructions for controllinga display to provide a graphical user interface. According to someimplementations, the instructions for controlling the display mayinclude instructions for making a presentation of conferenceparticipants. In some examples, the instructions for controlling thedisplay may include instructions for making a presentation of conferencesegments.

Some such implementations may involve receiving input corresponding to auser's interaction with the graphical user interface and processing theaudio data based, at least in part, on the input. In some examples, theinput may correspond to an indication of a target playback timeduration. Some such methods may involve providing the playback audiodata to a speaker system.

At least some aspects of the present disclosure may be implemented viaapparatus. For example, one or more devices may be capable ofperforming, at least in part, the methods disclosed herein. In someimplementations, an apparatus may include an interface system and acontrol system. The interface system may include a network interface, aninterface between the control system and a memory system, an interfacebetween the control system and another device and/or an external deviceinterface. The control system may include at least one of a generalpurpose single- or multi-chip processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, or discrete hardware components.

The control system may be capable of performing, at least in part, themethods disclosed herein. In some implementations, the control systemmay be capable of receiving teleconference audio data during ateleconference, via the interface system. The teleconference audio datamay include a plurality of individual uplink data packet streams. Eachuplink data packet stream may correspond to a telephone endpoint used byone or more teleconference participants. In some implementations, thecontrol system may be capable of sending to a memory system, via theinterface system, the teleconference audio data as individual uplinkdata packet streams.

According to some examples, the control system may be capable ofdetermining that a late data packet of an incomplete uplink data packetstream has been received from a telephone endpoint after a late packettime threshold. The late packet time threshold may be greater than orequal to a mouth-to-ear latency time threshold of the teleconference.The control system may be capable of adding the late data packet to theincomplete uplink data packet stream.

In some examples, the control system may be capable of determining thata missing data packet of an incomplete uplink data packet stream has notbeen received from a telephone endpoint within a missing packet timethreshold. The missing packet time threshold may, in some examples, begreater than the late packet time threshold. The control system may becapable of transmitting a request to the telephone endpoint, via theinterface system, to re-send the missing data packet, of receiving themissing data packet and of adding the missing data packet to theincomplete uplink data packet stream.

In some implementations, the individual uplink data packet streams maybe individual encoded uplink data packet streams. Some suchimplementations may involve sending the teleconference audio data to thememory system as individual encoded uplink data packet streams.

The interface system may include an interface between the control systemand at least part of the memory system. According to someimplementations, at least part of the memory system may be included inone or more or other devices, such as local or remote storage devices.In some implementations, the interface system may include a networkinterface and the control system may be capable of sending theteleconference audio data to the memory system via the networkinterface. According to some examples, however, the apparatus mayinclude at least part of the memory system.

In some examples, at least one of the uplink data packet streams mayinclude at least one data packet that was received after a mouth-to-earlatency time threshold of the teleconference and was therefore not usedfor reproducing audio data during the teleconference. According to someexamples, at least one of the uplink data packet streams may correspondto multiple teleconference participants and may include spatialinformation regarding each of the multiple participants. According tosome implementations, the control system may be capable of providingteleconference server functionality.

In some alternative implementations, an apparatus also may include aninterface system such as those described above. The apparatus also mayinclude a control system such as those described above. According tosome such implementations, the control system may be capable ofreceiving, via the interface system, recorded audio data for ateleconference. The recorded audio data may include an individual uplinkdata packet stream that corresponds to a telephone endpoint used by oneor more teleconference participants.

According to some examples, the control system may be capable ofanalyzing sequence number data of data packets in the individual uplinkdata packet stream. According to some such examples, the analyzingprocess may involve determining whether the individual uplink datapacket stream includes at least one out-of-order data packet. Thecontrol system may be capable of re-ordering the individual uplink datapacket stream according to the sequence number data if the uplink datapacket stream includes at least one out-of-order data packet.

In some instances, the control system may determine that at least onedata packet of the individual uplink data packet stream has beenreceived after a mouth-to-ear latency time threshold of theteleconference. According to some such examples, the control system maybe capable of receiving (e.g., via the interface system) teleconferencemetadata and indexing the individual uplink data packet stream based, atleast in part, on the teleconference metadata.

In some examples, the recorded audio data may include a plurality ofindividual encoded uplink data packet streams. Each of the individualencoded uplink data packet streams may correspond to a telephoneendpoint used by one or more teleconference participants. According tosome implementations, the control system may include a joint analysismodule capable of analyzing a plurality of individual uplink data packetstreams. According to some such examples, the control system may becapable of decoding the plurality of individual encoded uplink datapacket streams and providing a plurality of individual decoded uplinkdata packet streams to the joint analysis module.

In some implementations, the control system may include a speechrecognition module capable of recognizing speech. The speech recognitionmodule capable of generating speech recognition results data. Accordingto some examples, the control system may be capable of providing one ormore individual decoded uplink data packet streams to the speechrecognition module. According to some such examples, the speechrecognition module may be capable of providing the speech recognitionresults data to the joint analysis module.

According to some implementations, the joint analysis module may becapable of identifying keywords in the speech recognition results data.In some examples, the joint analysis module may be capable of indexingkeyword locations.

According to some examples, the control system may include a speakerdiarization module. In some instances, the control system may be capableof providing an individual decoded uplink data packet stream to thespeaker diarization module. The speaker diarization module may, forexample, be capable of identifying speech of each of multipleteleconference participants in an individual decoded uplink data packetstream. In some examples, the speaker diarization module may be capableof generating a speaker diary indicating times at which each of themultiple teleconference participants were speaking. The speakerdiarization module may be capable of providing the speaker diary to thejoint analysis module.

In some implementations, the joint analysis module may be capable ofdetermining conversational dynamics data. For example, theconversational dynamics data may include data indicating the frequencyand duration of conference participant speech, data indicating instancesof conference participant doubletalk during which at least twoconference participants are speaking simultaneously and/or dataindicating instances of conference participant conversations.

In some alternative implementations, an apparatus also may include aninterface system such as those described above. The apparatus also mayinclude a control system such as those described above. According tosome such implementations, the control system may be capable ofreceiving, via the interface system, audio data corresponding to arecording of a conference involving a plurality of conferenceparticipants. The audio data may include audio data from multipleendpoints. The audio data for each of the multiple endpoints may havebeen recorded separately. Alternatively, or additionally, the audio datamay include audio data from a single endpoint corresponding to multipleconference participants. The audio data may include spatial informationfor each conference participant of the multiple conference participants.

In some implementations, the control system may be capable of analyzingthe audio data to determine conversational dynamics data. In someexamples, the conversational dynamics data may include data indicatingthe frequency and duration of conference participant speech, dataindicating instances of conference participant doubletalk during whichat least two conference participants are speaking simultaneously and/orand data indicating instances of conference participant conversations.

According to some examples, the control system may be capable ofapplying the conversational dynamics data as one or more variables of aspatial optimization cost function of a vector describing a virtualconference participant position for each of the conference participantsin a virtual acoustic space. The control system may, for example, becapable of applying an optimization technique to the spatialoptimization cost function to determine a locally optimal solution. Thecontrol system may be capable of assigning the virtual conferenceparticipant positions in the virtual acoustic space based, at least inpart, on the locally optimal solution.

According to some implementations, the virtual acoustic space may bedetermined relative to a position of a virtual listener's head in thevirtual acoustic space. In some such implementations, the spatialoptimization cost function may apply a penalty for placing conferenceparticipants who are involved in conference participant doubletalk atvirtual conference participant positions that are on, or within apredetermined angular distance from, a cone of confusion. The cone ofconfusion may be defined relative to the position of the virtuallistener's head. Circular conical slices through the cone of confusionmay have identical inter-aural time differences.

In some examples, the spatial optimization cost function may apply apenalty for placing conference participants who are involved in aconference participant conversation with one another at virtualconference participant positions that are on, or within a predeterminedangular distance from, a cone of confusion. According to some examples,the spatial optimization cost function may apply a penalty for placingconference participants who speak frequently at virtual conferenceparticipant positions that are beside, behind, above, or below theposition of the virtual listener's head. In some implementations, thespatial optimization cost function may apply a penalty for placingconference participants who speak frequently at virtual conferenceparticipant positions that are farther from the position of the virtuallistener's head than the virtual conference participant positions ofconference participants who speak less frequently. However, according tosome implementations, assigning a virtual conference participantposition may involve selecting a virtual conference participant positionfrom a set of predetermined virtual conference participant positions.

In some alternative implementations, an apparatus also may include aninterface system such as those described above. The apparatus also mayinclude a control system such as those described above. According tosome such implementations, the control system may be capable ofreceiving, via the interface system, audio data corresponding to arecording of a conference involving a plurality of conferenceparticipants. The audio data may include audio data from multipleendpoints. The audio data for each of the multiple endpoints may havebeen recorded separately. Alternatively, or additionally, the audio datamay include audio data from a single endpoint corresponding to multipleconference participants. The audio data may include spatial informationfor each conference participant of the multiple conference participants.

According to some implementations, the control system may be capable ofrendering the conference participant speech data for each of theconference participants to a separate virtual conference participantposition in a virtual acoustic space. In some implementations, thecontrol system may be capable of scheduling the conference participantspeech for playback such that an amount of playback overlap between atleast two output talkspurts of the conference participant speech isgreater than an amount of original overlap between two correspondinginput talkspurts of the conference recording.

In some examples, the scheduling may be performed, at least in part,according to a set of perceptually-motivated rules. In someimplementations, the set of perceptually-motivated rules may include arule indicating that two output talkspurts of a single conferenceparticipant should not overlap in time. The set ofperceptually-motivated rules may include a rule indicating that twooutput talkspurts should not overlap in time if the two outputtalkspurts correspond to a single endpoint.

According to some implementations, given two consecutive inputtalkspurts A and B, A having occurred before B, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started. The setof perceptually-motivated rules may include a rule allowing the playbackof an output talkspurt corresponding to B to begin no sooner than a timeT before the playback of an output talkspurt corresponding to A iscomplete. In some such examples, T may be greater than zero.

According to some examples, the control system may be capable ofanalyzing the audio data to determine conversational dynamics data. Theconversational dynamics data may include data indicating the frequencyand duration of conference participant speech, data indicating instancesof conference participant doubletalk during which at least twoconference participants are speaking simultaneously and/or dataindicating instances of conference participant conversations.

In some examples, the control system may be capable of applying theconversational dynamics data as one or more variables of a spatialoptimization cost function of a vector describing the virtual conferenceparticipant position for each of the conference participants in thevirtual acoustic space. In some implementations, the control system maybe capable of applying an optimization technique to the spatialoptimization cost function to determine a locally optimal solution.According to some implementations, the control system may be capable ofassigning the virtual conference participant positions in the virtualacoustic space based, at least in part, on the locally optimal solution.

In some alternative implementations, an apparatus also may include aninterface system such as those described above. The apparatus also mayinclude a control system such as those described above. According tosome such implementations, the control system may be capable ofreceiving, via the interface system, audio data corresponding to arecording of a conference involving a plurality of conferenceparticipants. The audio data may include audio data from multipleendpoints. The audio data for each of the multiple endpoints may havebeen recorded separately. Alternatively, or additionally, the audio datamay include audio data from a single endpoint corresponding to multipleconference participants. The audio data may include information foridentifying conference participant speech for each conferenceparticipant of the multiple conference participants.

According to some implementations, the control system may be capable ofanalyzing conversational dynamics of the conference recording todetermine conversational dynamics data. In some examples, the controlsystem may be capable of searching the conference recording to determineinstances of each of a plurality of segment classifications. Each of thesegment classifications may be based, at least in part, on theconversational dynamics data.

According to some such examples, the control system may be capable ofsegmenting the conference recording into a plurality of segments. Eachof the segments may correspond with a time interval and at least one ofthe segment classifications. In some examples, the control system may becapable of performing the searching and segmenting processes multipletimes at different time scales.

In some implementations, the searching and segmenting processes may bebased, at least in part, on a hierarchy of segment classifications.According to some such implementations, the hierarchy of segmentclassifications may be based upon one or more criteria, such as a levelof confidence with which segments of a particular segment classificationmay be identified, a level of confidence with which a start time of asegment may be determined, a level of confidence with which an end timeof a segment may be determined and/or a likelihood that a particularsegment classification includes conference participant speechcorresponding to a conference topic.

In some examples, the control system may be capable of determininginstances of the segment classifications according to a set of rules.According to some such examples, the rules may be based on one or moreconversational dynamics data types, such as a doubletalk ratioindicating a fraction of speech time in a time interval during which atleast two conference participants are speaking simultaneously, a speechdensity metric indicating a fraction of the time interval during whichthere is any conference participant speech and/or a dominance metricindicating a fraction of total speech uttered by a dominant conferenceparticipant during the time interval. The dominant conferenceparticipant may be a conference participant who spoke the most duringthe time interval.

In some alternative implementations, an apparatus also may include aninterface system such as those described above. The apparatus also mayinclude a control system such as those described above. According tosome such implementations, the control system may be capable ofreceiving (e.g., via the interface system) speech recognition resultsdata for at least a portion of a recording of a conference involving aplurality of conference participants. In some examples, the speechrecognition results data may include a plurality of speech recognitionlattices and a word recognition confidence score for each of a pluralityof hypothesized words of the speech recognition lattices. The wordrecognition confidence score may, for example, correspond with alikelihood of a hypothesized word correctly corresponding with an actualword spoken by a conference participant during the conference.

In some implementations, the control system may be capable ofdetermining a primary word candidate and one or more alternative wordhypotheses for each of a plurality of hypothesized words in the speechrecognition lattices. The primary word candidate may have a wordrecognition confidence score indicating a higher likelihood of correctlycorresponding with the actual word spoken by the conference participantduring the conference than a word recognition confidence score of any ofthe one or more alternative word hypotheses.

According to some examples, the control system may be capable ofcalculating a term frequency metric of the primary word candidates andthe alternative word hypotheses. In some instances, the term frequencymetric may be based, at least in part, on a number of occurrences of ahypothesized word in the speech recognition lattices. Alternatively, oradditionally, the term frequency metric may be based, at least in part,on the word recognition confidence score.

According to some implementations, the control system may be capable ofsorting the primary word candidates and alternative word hypothesesaccording to the term frequency metric. According to some examples, thecontrol system may be capable of including the alternative wordhypotheses in an alternative hypothesis list. According to some suchexamples, the control system may be capable of re-scoring at least somehypothesized words of the speech recognition lattices according to thealternative hypothesis list.

In some examples, the control system may be capable of forming a wordlist. The word list may include primary word candidates and a termfrequency metric for each of the primary word candidates. According tosome examples, the control system may be capable of generating a topiclist of conference topics based, at least in part, on the word list. Insome implementations, generating the topic list may involve determininga hypernym of at least one word of the word list. Generating the topiclist may involve determining a topic score that includes a hypernymscore.

In some alternative implementations, an apparatus also may include aninterface system such as those described above. The apparatus also mayinclude a control system such as those described above. According tosome such implementations, the control system may be capable ofreceiving (e.g., via the interface system) audio data corresponding to arecording of at least one conference involving a plurality of conferenceparticipants. The audio data may include conference participant speechdata from multiple endpoints, recorded separately and/or conferenceparticipant speech data from a single endpoint corresponding to multipleconference participants, which may include spatial information for eachconference participant of the multiple conference participants.

According to some implementations, the control system may be capable ofdetermining search results corresponding to a search of the audio databased on one or more search parameters. The search results maycorrespond to at least two instances of conference participant speech inthe audio data. The at least two instances of conference participantspeech may include at least a first instance of speech uttered by afirst conference participant and at least a second instance of speechuttered by a second conference participant.

In some examples, the control system may be capable of rendering theinstances of conference participant speech to at least two differentvirtual conference participant positions of a virtual acoustic space,such that the first instance of speech is rendered to a first virtualconference participant position and the second instance of speech isrendered to a second virtual conference participant position. Accordingto some such examples, the control system may be capable of schedulingat least a portion of the instances of conference participant speech forsimultaneous playback, to produce playback audio data.

In some alternative implementations, an apparatus also may include aninterface system such as those described above. The apparatus also mayinclude a control system such as those described above. According tosome such implementations, the control system may be capable ofreceiving (e.g., via the interface system) audio data corresponding to arecording of a conference. The audio data may include data correspondingto conference participant speech of each of a plurality of conferenceparticipants.

According to some examples, the control system may be capable ofselecting only a portion of the conference participant speech asplayback audio data. According to some such examples, the control systemmay be capable of providing (e.g., via the interface system) theplayback audio data to a speaker system for playback.

According to some implementations, the selecting process may involve atopic selection process of selecting conference participant speech forplayback according to estimated relevance of the conference participantspeech to one or more conference topics. In some implementations, theselecting process may involve a topic selection process of selectingconference participant speech for playback according to estimatedrelevance of the conference participant speech to one or more topics ofa conference segment.

In some instances, the selecting process may involve removing inputtalkspurts having an input talkspurt time duration that is below athreshold input talkspurt time duration. According to some examples, theselecting process may involve a talkspurt filtering process of removinga portion of input talkspurts having an input talkspurt time durationthat is at or above the threshold input talkspurt time duration.

Alternatively, or additionally, the selecting process may involve anacoustic feature selection process of selecting conference participantspeech for playback according to at least one acoustic feature. In someexamples, the selecting may involve an iterative process.

According to some examples, the control system may be capable ofreceiving (e.g., via the interface system) an indication of a targetplayback time duration. According to some such examples, the selectingprocess may involve making a time duration of the playback audio datawithin a threshold time difference and/or or a threshold time percentageof the target playback time duration. In some examples, the timeduration of the playback audio data may be determined, at least in part,by multiplying a time duration of at least one selected portion of theconference participant speech by an acceleration coefficient.

Some or all of the methods described herein may be performed by one ormore devices according to instructions (e.g., software) stored onnon-transitory media. Such non-transitory media may include memorydevices such as those described herein, including but not limited torandom access memory (RAM) devices, read-only memory (ROM) devices, etc.Accordingly, various innovative aspects of the subject matter describedin this disclosure can be implemented in a non-transitory medium havingsoftware stored thereon. The software may, for example, includeinstructions for controlling at least one device to process audio data.The software may, for example, be executable by one or more componentsof a control system such as those disclosed herein.

According to some examples, the software may include instructions forreceiving teleconference audio data during a teleconference. Theteleconference audio data may include a plurality of individual uplinkdata packet streams. Each uplink data packet stream may correspond to atelephone endpoint used by one or more teleconference participants. Insome implementations the software may include instructions for sendingthe teleconference audio data to a memory system as individual uplinkdata packet streams.

In some examples, the individual uplink data packet streams may beindividual encoded uplink data packet streams. According to someexamples, at least one of the uplink data packet streams may include atleast one data packet that was received after a mouth-to-ear latencytime threshold of the teleconference and was therefore not used forreproducing audio data during the teleconference. According to some suchexamples, at least one of the uplink data packet streams may correspondto multiple teleconference participants and may include spatialinformation regarding each of the multiple participants.

In some implementations, the software may include instructions forreceiving audio data corresponding to a recording of a conferenceinvolving a plurality of conference participants. According to someexamples, the audio data may include audio data from multiple endpoints.The audio data for each of the multiple endpoints may have been recordedseparately. Alternatively, or additionally, the audio data may includeaudio data from a single endpoint corresponding to multiple conferenceparticipants and may include spatial information for each conferenceparticipant of the multiple conference participants.

According to some implementations, the software may include instructionsfor analyzing the audio data to determine conversational dynamics data.The conversational dynamics data may, for example, include dataindicating the frequency and duration of conference participant speech,data indicating instances of conference participant doubletalk duringwhich at least two conference participants are speaking simultaneouslyand/or data indicating instances of conference participantconversations.

In some instances, the software may include instructions for applyingthe conversational dynamics data as one or more variables of a spatialoptimization cost function of a vector describing a virtual conferenceparticipant position for each of the conference participants in avirtual acoustic space. According to some examples, the software mayinclude instructions for applying an optimization technique to thespatial optimization cost function to determine a locally optimalsolution. According to some such examples, the software may includeinstructions for assigning the virtual conference participant positionsin the virtual acoustic space based, at least in part, on the locallyoptimal solution.

In some implementations, the virtual acoustic space may be determinedrelative to a position of a virtual listener's head in the virtualacoustic space. According to some such implementations, the spatialoptimization cost function may apply a penalty for placing conferenceparticipants who are involved in conference participant doubletalk atvirtual conference participant positions that are on, or within apredetermined angular distance from, a cone of confusion definedrelative to the position of the virtual listener's head. Circularconical slices through the cone of confusion may have identicalinter-aural time differences. In some examples, the spatial optimizationcost function may apply a penalty for placing conference participantswho are involved in a conference participant conversation with oneanother at virtual conference participant positions that are on, orwithin a predetermined angular distance from, a cone of confusion.

According to some examples, analyzing the audio data may involvedetermining which conference participants, if any, have perceptuallysimilar voices. In some such examples, the spatial optimization costfunction may apply a penalty for placing conference participants withperceptually similar voices at virtual conference participant positionsthat are on, or within a predetermined angular distance from, a cone ofconfusion.

In some examples, the spatial optimization cost function may apply apenalty for placing conference participants who speak frequently atvirtual conference participant positions that are beside, behind, above,or below the position of the virtual listener's head. In some instances,the spatial optimization cost function may apply a penalty for placingconference participants who speak frequently at virtual conferenceparticipant positions that are farther from the position of the virtuallistener's head than the virtual conference participant positions ofconference participants who speak less frequently. In someimplementations, the spatial optimization cost function may apply apenalty for placing conference participants who speak infrequently atvirtual conference participant positions that are not beside, behind,above or below the position of the virtual listener's head.

According to some examples, the optimization technique may involve agradient descent technique, conjugate gradient technique, Newton'smethod, the Broyden-Fletcher-Goldfarb-Shanno algorithm; a geneticalgorithm, an algorithm for simulated annealing, an ant colonyoptimization method and/or a Monte Carlo method. In some examples,assigning a virtual conference participant position may involveselecting a virtual conference participant position from a set ofpredetermined virtual conference participant positions.

In some implementations, the software may include instructions forreceiving audio data corresponding to a recording of a conferenceinvolving a plurality of conference participants. According to someexamples, the audio data may include audio data from multiple endpoints.The audio data for each of the multiple endpoints may have been recordedseparately. Alternatively, or additionally, the audio data may includeaudio data from a single endpoint corresponding to multiple conferenceparticipants and may include spatial information for each conferenceparticipant of the multiple conference participants.

According to some implementations, the software may include instructionsfor rendering the conference participant speech data in a virtualacoustic space such that each of the conference participants has arespective different virtual conference participant position. In someexamples, the software may include instructions for scheduling theconference participant speech for playback such that an amount ofplayback overlap between at least two output talkspurts of theconference participant speech is different from (e.g., greater than) anamount of original overlap between two corresponding input talkspurts ofthe conference recording.

According to some examples, the software may include instructions forperforming the scheduling process, at least in part, according to a setof perceptually-motivated rules. In some implementations, the set ofperceptually-motivated rules may include a rule indicating that twooutput talkspurts of a single conference participant should not overlapin time. The set of perceptually-motivated rules may include a ruleindicating that two output talkspurts should not overlap in time if thetwo output talkspurts correspond to a single endpoint.

According to some implementations, given two consecutive inputtalkspurts A and B, A having occurred before B, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started. The setof perceptually-motivated rules may include a rule allowing the playbackof an output talkspurt corresponding to B to begin no sooner than a timeT before the playback of an output talkspurt corresponding to A iscomplete. In some such examples, T may be greater than zero.

According to some implementations, the set of perceptually-motivatedrules may include a rule allowing the concurrent playback of entirepresentations from different conference participants. In someimplementations, a presentation may correspond with a time interval ofthe conference participant speech during which a speech density metricis greater than or equal to a silence threshold, a doubletalk ratio isless than or equal to a discussion threshold and a dominance metric isgreater than a presentation threshold. The doubletalk ratio may indicatea fraction of speech time in the time interval during which at least twoconference participants are speaking simultaneously. The speech densitymetric may indicate a fraction of the time interval during which thereis any conference participant speech. The dominance metric may indicatea fraction of total speech uttered by a dominant conference participantduring the time interval. The dominant conference participant may be aconference participant who spoke the most during the time interval.

In some examples, at least some of the conference participant speech maybe scheduled to be played back at a faster rate than the rate at whichthe conference participant speech was recoded. According to some suchexamples, scheduling the playback of the speech at the faster rate maybe accomplished by using a WSOLA (Waveform Similarity Based Overlap Add)technique.

According to some implementations, the software may include instructionsfor analyzing the audio data to determine conversational dynamics data.The conversational dynamics data may include data indicating thefrequency and duration of conference participant speech, data indicatinginstances of conference participant doubletalk during which at least twoconference participants are speaking simultaneously and/or dataindicating instances of conference participant conversations. In someexamples, the software may include instructions for applying theconversational dynamics data as one or more variables of a spatialoptimization cost function of a vector describing the virtual conferenceparticipant position for each of the conference participants in thevirtual acoustic space. In some implementations, the software mayinclude instructions for applying an optimization technique to thespatial optimization cost function to determine a locally optimalsolution and assigning the virtual conference participant positions inthe virtual acoustic space based, at least in part, on the locallyoptimal solution.

In some implementations, the software may include instructions forreceiving audio data corresponding to a recording of a conferenceinvolving a plurality of conference participants. According to someexamples, the audio data may include audio data from multiple endpoints.The audio data for each of the multiple endpoints may have been recordedseparately. Alternatively, or additionally, the audio data may includeaudio data from a single endpoint corresponding to multiple conferenceparticipants and may include information for identifying conferenceparticipant speech for each conference participant of the multipleconference participants.

According to some examples, the software may include instructions foranalyzing conversational dynamics of the conference recording todetermine conversational dynamics data. In some examples, the softwaremay include instructions for searching the conference recording todetermine instances of each of a plurality of segment classifications.Each of the segment classifications may be based, at least in part, onthe conversational dynamics data. According to some such examples, thesoftware may include instructions for segmenting the conferencerecording into a plurality of segments. Each of the segments maycorrespond with a time interval and at least one of the segmentclassifications. According to some implementations, the software mayinclude instructions for performing the searching and segmentingprocesses multiple times at different time scales.

In some examples, the searching and segmenting processes may be based,at least in part, on a hierarchy of segment classifications. Accordingto some such examples, the hierarchy of segment classifications may bebased, at least in part, upon a level of confidence with which segmentsof a particular segment classification may be identified, a level ofconfidence with which a start time of a segment may be determined, alevel of confidence with which an end time of a segment may bedetermined and/or a likelihood that a particular segment classificationincludes conference participant speech corresponding to a conferencetopic.

According to some implementations, the software may include instructionsfor determining instances of the segment classifications according to aset of rules. In some such implementations, the rules may be based onone or more conversational dynamics data types, such as a doubletalkratio indicating a fraction of speech time in a time interval duringwhich at least two conference participants are speaking simultaneously,a speech density metric indicating a fraction of the time intervalduring which there is any conference participant speech and/or adominance metric indicating a fraction of total speech uttered by adominant conference participant during the time interval. The dominantconference participant may be a conference participant who spoke themost during the time interval.

In some implementations, the software may include instructions forreceiving speech recognition results data for at least a portion of aconference recording of a conference involving a plurality of conferenceparticipants. In some examples, the speech recognition results data mayinclude a plurality of speech recognition lattices. The speechrecognition results data may include a word recognition confidence scorefor each of a plurality of hypothesized words of the speech recognitionlattices. According to some such examples, the word recognitionconfidence score may correspond with a likelihood of a hypothesized wordcorrectly corresponding with an actual word spoken by a conferenceparticipant during the conference.

According to some examples, the software may include instructions fordetermining a primary word candidate and one or more alternative wordhypotheses for each of a plurality of hypothesized words in the speechrecognition lattices. The primary word candidate may have a wordrecognition confidence score indicating a higher likelihood of correctlycorresponding with the actual word spoken by the conference participantduring the conference than a word recognition confidence score of any ofthe one or more alternative word hypotheses.

According to some implementations, the software may include instructionsfor calculating a term frequency metric of the primary word candidatesand the alternative word hypotheses. In some such implementations, theterm frequency metric may be based, at least in part, on a number ofoccurrences of a hypothesized word in the speech recognition latticesand the word recognition confidence score.

In some examples, the software may include instructions for sorting theprimary word candidates and alternative word hypotheses according to theterm frequency metric. According to some such examples, the software mayinclude instructions for including the alternative word hypotheses in analternative hypothesis list. In some such implementations, the softwaremay include instructions for re-scoring at least some hypothesized wordsof the speech recognition lattices according to the alternativehypothesis list.

According to some examples, the software may include instructions forforming a word list. The word list may, for example, include primaryword candidates and a term frequency metric for each of the primary wordcandidates. According to some such examples, the software may includeinstructions for generating a topic list of conference topics based, atleast in part, on the word list.

In some implementations, generating the topic list may involvedetermining a hypernym of at least one word of the word list. Accordingto some such implementations, generating the topic list may involvedetermining a topic score that includes a hypernym score.

In some implementations, the software may include instructions forreceiving audio data corresponding to a recording of at least oneconference involving a plurality of conference participants. The audiodata may include conference participant speech data from multipleendpoints, recorded separately and/or conference participant speech datafrom a single endpoint corresponding to multiple conferenceparticipants, which may include spatial information for each conferenceparticipant of the multiple conference participants.

According to some examples, the software may include instructions fordetermining search results based on a search of the audio data. Thesearch may be, or may have been, based on one or more search parameters.The search results may correspond to at least two instances ofconference participant speech in the audio data. The instances ofconference participant speech may, for example, include talkspurtsand/or portions of talkspurts. The instances of conference participantspeech may include a first instance of speech uttered by a firstconference participant and a second instance of speech uttered by asecond conference participant.

In some examples, the software may include instructions for renderingthe instances of conference participant speech to at least two differentvirtual conference participant positions of a virtual acoustic space,such that the first instance of speech is rendered to a first virtualconference participant position and the second instance of speech isrendered to a second virtual conference participant position. Accordingto some such examples, the software may include instructions forscheduling at least a portion of the instances of conference participantspeech for simultaneous playback, to produce playback audio data.

According to some implementations, determining the search results mayinvolve receiving search results. For example, determining the searchresults may involve receiving the search results resulting from a searchperformed by another device, e.g., by a server.

However, in some implementations determining the search results mayinvolve performing a search. According to some examples, determining thesearch results may involve performing a concurrent search of the audiodata regarding multiple features. According to some implementations, themultiple features may include two or more features selected from a setof features. The set of features may include words, conference segments,time, conference participant emotion, endpoint location and/or endpointtype. In some implementations, determining the search results mayinvolve performing a search of audio data that corresponds to recordingsof multiple conferences. In some examples, the scheduling process mayinvolve scheduling the instances of conference participant speech forplayback based, at least in part, on a search relevance metric.

According to some examples, the software may include instructions formodifying a start time or an end time of at least one of the instancesof conference participant speech. In some examples, the modifyingprocess may involve expanding a time interval corresponding to aninstance of conference participant speech. According to some examples,the modifying process may involve merging two or more instances ofconference participant speech, corresponding with a single conferenceendpoint, that overlap in time after the expanding.

In some examples, the software may include instructions for schedulingan instance of conference participant speech that did not previouslyoverlap in time to be played back overlapped in time. Alternatively, oradditionally, the software may include instructions for scheduling aninstance of conference participant speech that was previously overlappedin time to be played back further overlapped in time.

According to some implementations, the scheduling may be performedaccording to a set of perceptually-motivated rules. In someimplementations, the set of perceptually-motivated rules may include arule indicating that two output talkspurts of a single conferenceparticipant should not overlap in time. The set ofperceptually-motivated rules may include a rule indicating that twooutput talkspurts should not overlap in time if the two outputtalkspurts correspond to a single endpoint.

According to some implementations, given two consecutive inputtalkspurts A and B, A having occurred before B, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started. The setof perceptually-motivated rules may include a rule allowing the playbackof an output talkspurt corresponding to B to begin no sooner than a timeT before the playback of an output talkspurt corresponding to A iscomplete. In some such examples, T may be greater than zero.

In some implementations, the software may include instructions forreceiving audio data corresponding to a recording of a conference. Theaudio data may include data corresponding to conference participantspeech of each of a plurality of conference participants. In someexamples, the software may include instructions for selecting only aportion of the conference participant speech as playback audio data.

According to some implementations, the selecting process may involve atopic selection process of selecting conference participant speech forplayback according to estimated relevance of the conference participantspeech to one or more conference topics. In some implementations, theselecting process may involve a topic selection process of selectingconference participant speech for playback according to estimatedrelevance of the conference participant speech to one or more topics ofa conference segment.

In some instances, the selecting process may involve removing inputtalkspurts having an input talkspurt time duration that is below athreshold input talkspurt time duration. According to some examples, theselecting process may involve a talkspurt filtering process of removinga portion of input talkspurts having an input talkspurt time durationthat is at or above the threshold input talkspurt time duration.

Alternatively, or additionally, the selecting process may involve anacoustic feature selection process of selecting conference participantspeech for playback according to at least one acoustic feature. In someexamples, the selecting may involve an iterative process. Some suchimplementations may involve providing the playback audio data to aspeaker system for playback.

According to some implementations, the software may include instructionsfor receiving an indication of a target playback time duration.According to some such examples, the selecting process may involvemaking a time duration of the playback audio data within a thresholdtime difference and/or or a threshold time percentage of the targetplayback time duration. In some examples, the time duration of theplayback audio data may be determined, at least in part, by multiplyinga time duration of at least one selected portion of the conferenceparticipant speech by an acceleration coefficient.

According to some examples, the audio data may include conferenceparticipant speech data from multiple endpoints, recorded separately orconference participant speech data from a single endpoint correspondingto multiple conference participants, which may include spatialinformation for each conference participant of the multiple conferenceparticipants. According to some such examples, the software may includeinstructions for rendering the playback audio data in a virtual acousticspace such that each of the conference participants whose speech isincluded in the playback audio data has a respective different virtualconference participant position.

According to some implementations, the selecting process may involve atopic section process. According to some such examples, the topicsection process may involve receiving a topic list of conference topicsand determining a list of selected conference topics. The list ofselected conference topics may be a subset of the conference topics.

In some examples, the software may include instructions for receivingtopic ranking data, which may indicate an estimated relevance of eachconference topic on the topic list. Determining the list of selectedconference topics may be based, at least in part, on the topic rankingdata.

According to some implementations, the selecting process may involve atalkspurt filtering process. The talkspurt filtering process may, forexample, involve removing an initial portion of an input talkspurt. Theinitial portion may be a time interval from an input talkspurt starttime to an output talkspurt start time. In some instances, the softwaremay include instructions for calculating an output talkspurt timeduration based, at least in part, on an input talkspurt time duration.

According to some such examples, the software may include instructionsfor determining whether the output talkspurt time duration exceeds anoutput talkspurt time threshold. If it is determined that the outputtalkspurt time duration exceeds an output talkspurt time threshold, thetalkspurt filtering process may involve generating multiple instances ofconference participant speech for a single input talkspurt. According tosome such examples, at least one of the multiple instances of conferenceparticipant speech may have an end time that corresponds with an inputtalkspurt end time.

According to some implementations, the selecting process may involve anacoustic feature selection process. In some examples, the acousticfeature selection process may involve determining at least one acousticfeature, such as pitch variance, speech rate and/or loudness.

In some implementations, the software may include instructions formodifying a start time or an end time of at least one of the instancesof conference participant speech. In some examples, the modifyingprocess may involve expanding a time interval corresponding to aninstance of conference participant speech. According to some examples,the modifying process may involve merging two or more instances ofconference participant speech, corresponding with a single conferenceendpoint, that overlap in time after the expanding.

In some examples, the software may include instructions for schedulingan instance of conference participant speech that did not previouslyoverlap in time to be played back overlapped in time. Alternatively, oradditionally, the software may include instructions for scheduling aninstance of conference participant speech that was previously overlappedin time to be played back further overlapped in time.

According to some examples, the scheduling may be performed according toa set of perceptually-motivated rules. In some implementations, the setof perceptually-motivated rules may include a rule indicating that twooutput talkspurts of a single conference participant should not overlapin time. The set of perceptually-motivated rules may include a ruleindicating that two output talkspurts should not overlap in time if thetwo output talkspurts correspond to a single endpoint.

According to some implementations, given two consecutive inputtalkspurts A and B, A having occurred before B, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started. The setof perceptually-motivated rules may include a rule allowing the playbackof an output talkspurt corresponding to B to begin no sooner than a timeT before the playback of an output talkspurt corresponding to A iscomplete. In some such examples, T may be greater than zero. Someimplementations may involve scheduling instances of conferenceparticipant speech for playback based, at least in part, on a searchrelevance metric.

According to some implementations, the software may include instructionsfor analyzing the audio data to determine conversational dynamics data.The conversational dynamics data may, for example, include dataindicating the frequency and duration of conference participant speech,data indicating instances of conference participant doubletalk duringwhich at least two conference participants are speaking simultaneouslyand/or data indicating instances of conference participantconversations.

In some instances, the software may include instructions for applyingthe conversational dynamics data as one or more variables of a spatialoptimization cost function of a vector describing a virtual conferenceparticipant position for each of the conference participants in avirtual acoustic space. According to some examples, the software mayinclude instructions for applying an optimization technique to thespatial optimization cost function to determine a locally optimalsolution. According to some such examples, the software may includeinstructions for assigning the virtual conference participant positionsin the virtual acoustic space based, at least in part, on the locallyoptimal solution.

In some implementations, the software may include instructions forcontrolling a display to provide a graphical user interface. Accordingto some implementations, the instructions for controlling the displaymay include instructions for making a presentation of conferenceparticipants. In some examples, the instructions for controlling thedisplay may include instructions for making a presentation of conferencesegments.

In some examples, the software may include instructions for receivinginput corresponding to a user's interaction with the graphical userinterface and processing the audio data based, at least in part, on theinput. In some examples, the input may correspond to an indication of atarget playback time duration. According to some implementations, thesoftware may include instructions for providing the playback audio datato a speaker system.

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Note thatthe relative dimensions of the following figures may not be drawn toscale.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows examples of components of a teleconferencing system.

FIG. 1B is a block diagram that shows examples of components of anapparatus capable of implementing various aspects of this disclosure.

FIG. 1C is a flow diagram that outlines one example of a method that maybe performed by the apparatus of FIG. 1B.

FIG. 2A shows additional examples of components of a teleconferencingsystem.

FIG. 2B shows examples of packet trace files and conference metadata.

FIG. 3A is a block diagram that shows examples of components of anapparatus capable of implementing various aspects of this disclosure.

FIG. 3B is a flow diagram that outlines one example of a method that maybe performed by the apparatus of FIG. 3A.

FIG. 3C shows additional examples of components of a teleconferencingsystem.

FIG. 4 shows examples of components of an uplink analysis module.

FIG. 5 shows examples of components of a joint analysis module.

FIG. 6 shows examples of components of a playback system and associatedequipment.

FIG. 7 shows an example of an in-person conference implementation.

FIG. 8 is a flow diagram that outlines one example of a method accordingto some implementations of this disclosure.

FIG. 9 shows an example of a virtual listener's head and a cone ofconfusion in a virtual acoustic space.

FIG. 10 shows an example of initial virtual conference participantpositions in a virtual acoustic space.

FIG. 11 shows examples of final virtual conference participant positionsin a virtual acoustic space.

FIG. 12 is a flow diagram that outlines one example of a methodaccording to some implementations of this disclosure.

FIG. 13 is a block diagram that shows an example of scheduling aconference recording for playback during an output time interval that isless than an input time interval.

FIG. 14 shows an example of maintaining an analogous temporalrelationship between overlapped input talkspurts and overlapped outputtalkspurts.

FIG. 15 shows an example of determining an amount of overlap for inputtalkspurts that did not overlap.

FIG. 16 is a block diagram that shows an example of applying aperceptually-motivated rule to avoid overlap of output talkspurts fromthe same endpoint.

FIG. 17 is a block diagram that shows an example of a system capable ofscheduling concurrent playback of entire presentations from differentconference participants.

FIG. 18A is a flow diagram that outlines one example of a conferencesegmentation method.

FIG. 18B shows an example of a system for performing, at least in part,some of the conference segmentation methods and related methodsdescribed herein.

FIG. 19 outlines an initial stage of a segmentation process according tosome implementations disclosed herein.

FIG. 20 outlines a subsequent stage of a segmentation process accordingto some implementations disclosed herein.

FIG. 21 outlines a subsequent stage of a segmentation process accordingto some implementations disclosed herein.

FIG. 22 outlines operations that may be performed by a segmentclassifier according to some implementations disclosed herein.

FIG. 23 shows an example of a longest segment search process accordingto some implementations disclosed herein.

FIG. 24 is a flow diagram that outlines blocks of some topic analysismethods disclosed herein.

FIG. 25 shows examples of topic analysis module elements.

FIG. 26 shows an example of an input speech recognition lattice.

FIG. 27, which includes FIGS. 27A and 27B, shows an example of a portionof a small speech recognition lattice after pruning.

FIG. 28, which includes FIGS. 28A and 28B, shows an example of a userinterface that includes a word cloud for an entire conference recording.

FIG. 29, which includes FIGS. 29A and 29B, shows an example of a userinterface that includes a word cloud for each of a plurality ofconference segments.

FIG. 30 is a flow diagram that outlines blocks of some playback controlmethods disclosed herein.

FIG. 31 shows an example of selecting a topic from a word cloud.

FIG. 32 shows an example of selecting both a topic from a word cloud anda conference participant from a list of conference participants.

FIG. 33 is a flow diagram that outlines blocks of some topic analysismethods disclosed herein.

FIG. 34 is a block diagram that shows examples of search systemelements.

FIG. 35 shows example playback scheduling unit, merging unit andplayback scheduling unit functionality.

FIG. 36 shows an example of a graphical user interface that may be usedto implement some aspects of this disclosure.

FIG. 37 shows an example of a graphical user interface being used for amulti-dimensional conference search.

FIG. 38A shows an example portion of a contextually augmented speechrecognition lattice.

FIGS. 38B and 38C show examples of keyword spotting index datastructures that may be generated by using a contextually augmentedspeech recognition lattice such as that shown in FIG. 38A as input.

FIG. 39 shows an example of clustered contextual features.

FIG. 40 is a block diagram that shows an example of a hierarchical indexthat is based on time.

FIG. 41 is a block diagram that shows an example of contextual keywordsearching.

FIG. 42 shows an example of a top-down timestamp-based hash search.

FIG. 43 is a flow diagram that outlines blocks of some methods ofselecting only a portion of conference participant speech for playback.

FIG. 44 shows an example of a selective digest module.

FIG. 45 shows examples of elements of a selective digest module.

FIG. 46 shows an example of a system for applying a selective digestmethod to a segmented conference.

FIG. 47 shows examples of blocks of a selector module according to someimplementations.

FIGS. 48A and 48B show examples of blocks of a selector module accordingto some alternative implementations.

FIG. 49 shows examples of blocks of a selector module according to otheralternative implementations.

Like reference numbers and designations in the various drawings indicatelike elements.

DESCRIPTION OF EXAMPLE EMBODIMENTS

The following description is directed to certain implementations for thepurposes of describing some innovative aspects of this disclosure, aswell as examples of contexts in which these innovative aspects may beimplemented. However, the teachings herein can be applied in variousdifferent ways. For example, while various implementations are describedin terms of particular examples of audio data processing in theteleconferencing context, the teachings herein are widely applicable toother known audio data processing contexts, such as processing audiodata corresponding to in-person conferences. Such conferences may, forexample, include academic and/or professional conferences, stock brokercalls, doctor/client visits, personal diarization (e.g., via a portablerecording device such as a wearable recording device), etc.

Moreover, the described embodiments may be implemented in a variety ofhardware, software, firmware, etc. For example, aspects of the presentapplication may be embodied, at least in part, in an apparatus (ateleconferencing bridge and/or server, an analysis system, a playbacksystem, a personal computer, such as a desktop, laptop, or tabletcomputer, a telephone, such as a desktop telephone, a smart phone orother cellular telephone, a television set-top box, a digital mediaplayer, etc.), a method, a computer program product, in a system thatincludes more than one apparatus (including but not limited to ateleconferencing system), etc. Accordingly, aspects of the presentapplication may take the form of a hardware embodiment, a softwareembodiment (including firmware, resident software, microcodes, etc.)and/or an embodiment combining both software and hardware aspects. Suchembodiments may be referred to herein as a “circuit,” a “module” or“engine.” Some aspects of the present application may take the form of acomputer program product embodied in one or more non-transitory mediahaving computer readable program code embodied thereon. Suchnon-transitory media may, for example, include a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a portable compact discread-only memory (CD-ROM), an optical storage device, a magnetic storagedevice, or any suitable combination of the foregoing. Accordingly, theteachings of this disclosure are not intended to be limited to theimplementations shown in the figures and/or described herein, butinstead have wide applicability.

Some aspects of the present disclosure involve the recording, processingand playback of audio data corresponding to conferences, such asteleconferences. In some teleconference implementations, the audioexperience heard when a recording of the conference is played back maybe substantially different from the audio experience of an individualconference participant during the original teleconference. In someimplementations, the recorded audio data may include at least some audiodata that was not available during the teleconference. In some examples,the spatial and/or temporal characteristics of the played-back audiodata may be different from that of the audio heard by participants ofthe teleconference.

FIG. 1A shows examples of components of a teleconferencing system. Thecomponents of the teleconferencing system 100 may be implemented viahardware, via software stored on non-transitory media, via firmwareand/or by combinations thereof. The types and numbers of componentsshown in FIG. 1A are merely shown by way of example. Alternativeimplementations may include more, fewer and/or different components.

In this example, the teleconferencing system 100 includes ateleconferencing apparatus 200 that is capable of providing thefunctionality of a teleconferencing server according to a packet-basedprotocol, which is a VoIP (Voice over Internet Protocol) in thisimplementation. At least some of the telephone endpoints 1 may includefeatures that allow conference participants to use a softwareapplication running on a desktop or laptop computer, a smartphone, adedicated VoIP telephone device or another such device to act as atelephony client, connecting to the teleconferencing server over theInternet.

However, some of the telephone endpoints 1 may not include suchfeatures. Accordingly, the teleconferencing system 100 may provideaccess via the PSTN (Public Switched Telephone Network), e.g., in theform of a bridge that transforms the traditional telephony streams fromthe PSTN into VoIP data packet streams.

In some implementations, during a teleconference the teleconferencingapparatus 200 receives a plurality of individual uplink data packetstreams 7 and transmits a plurality of individual downlink data packetstreams 8 to and from a plurality of telephone endpoints 1. Thetelephone endpoints 1 may include telephones, personal computers, mobileelectronic devices (e.g., cellular telephones, smart phones, tablets,etc.) or other appropriate devices. Some of the telephone endpoints 1may include headsets, such as stereophonic headsets. Other telephoneendpoints 1 may include a traditional telephone handset. Still othertelephone endpoints 1 may include teleconferencing speaker phones, whichmay be used by multiple conference participants. Accordingly, theindividual uplink data packet streams 7 received from some suchtelephone endpoints 1 may include teleconference audio data frommultiple conference participants.

In this example, one of the telephone endpoints includes ateleconference recording module 2. Accordingly, the teleconferencerecording module 2 receives a downlink data packet stream 8 but does nottransmit an uplink data packet stream 7. Although shown as a separateapparatus in FIG. 1A, teleconference recording module 2 may beimplemented as hardware, software and/or firmware. In some examples, theteleconference recording module 2 may be implemented via a hardware,software and/or firmware of a teleconferencing server. However, theteleconference recording module 2 is purely optional. Otherimplementations of the teleconferencing system 100 do not include theteleconference recording module 2.

Voice transmission over packet networks is subject to delay variation,commonly known as jitter. Jitter may, for example, be measured in termsof inter-arrival time (IAT) variation or packet delay variation (PDV).IAT variation may be measured according to the receive time differenceof adjacent packets. PDV may, for example, be measured by reference totime intervals from a datum or “anchor” packet receive time. In InternetProtocol (IP)-based networks, a fixed delay can be attributed toalgorithmic, processing and propagation delays due to material and/ordistance, whereas a variable delay may be caused by the fluctuation ofIP network traffic, different transmission paths over the Internet, etc.

Teleconferencing servers generally rely on a “jitter buffer” to counterthe negative impact of jitter. By introducing an additional delaybetween the time a packet of audio data is received and the time thatthe packet is reproduced, a jitter buffer can transform an uneven flowof arriving packets into a more regular flow of packets, such that delayvariations will not cause perceptual sound quality degradation to theend users. However, voice communication is highly delay-sensitive.According to ITU Recommendation G.114, for example, one-way delay(sometimes referred to herein as a “mouth-to-ear latency timethreshold”) should be kept below 150 milliseconds (ms) for normalconversation, with above 400 ms being considered unacceptable. Typicallatency targets for teleconferencing are lower than 150 ms, e.g., 100 msor below.

The low latency requirement may place an upper limit on how long theteleconferencing apparatus 200 may wait for an expected uplink datapacket to arrive without annoying conference participants. Uplink datapackets that arrive too late for reproduction during a teleconferencewill not be provided to the telephone endpoints 1 or the teleconferencerecording module 2. Instead, the corresponding downlink data packetstreams 8 will be provided to the telephone endpoints 1 and theteleconference recording module 2 with missing or late data packetsdropped. In the context of this disclosure, a “late” data packet is adata packet that arrived too late to be provided to the telephoneendpoints 1 or the teleconference recording module 2 during ateleconference.

However, in various implementations disclosed herein, theteleconferencing apparatus 200 may be capable of recording more completeuplink data packet streams 7. In some implementations, theteleconferencing apparatus 200 may be capable of including late datapackets in the recorded uplink data packet streams 7 that were receivedafter a mouth-to-ear latency time threshold of the teleconference andtherefore were not used for reproducing audio data to conferenceparticipants during the teleconference. In some such implementations,the teleconferencing apparatus 200 may be capable of determining that alate data packet of an incomplete uplink data packet stream has not beenreceived from a telephone endpoint within a late packet time threshold.The late packet time threshold may be greater than or equal to amouth-to-ear latency time threshold of the teleconference. For example,in some implementations the late packet time threshold may be greaterthan or equal to 200 ms, 400 ms, 500 ms, 1 second or more.

In some examples, the teleconferencing apparatus 200 may be capable ofdetermining that a data packet of an incomplete uplink data packetstream has not been received from a telephone endpoint within a missingpacket time threshold, greater than the late packet time threshold. Insome such examples, the teleconferencing apparatus 200 may be capable oftransmitting a request, to the telephone endpoint, to re-send a missingdata packet. Like the late data packets, the missing data packets wouldnot have been recorded by the teleconference recording module 2. Themissing packet time threshold may, in some implementations, be hundredsof milliseconds or even several seconds, e.g., 5 seconds, 10 seconds, 20seconds, 30 seconds, etc. In some implementations, the missing packettime threshold may be one minute or longer, e.g., 2, minutes, 3 minutes,4, minutes, 5 minutes, etc.

In this example, the teleconferencing apparatus 200 is capable ofrecording the individual uplink data packet streams 7 and providing themto the conference recording database 3 as individual uplink data packetstreams. The conference recording database 3 may be stored in one ormore storage systems, which may or may not be in the same location asthe teleconferencing apparatus 200, depending on the particularimplementation. Accordingly, in some implementations the individualuplink data packet streams that are recorded by the teleconferencingapparatus 200 and stored in the conference recording database 3 may bemore complete than the data packet streams available during theteleconference.

In the implementation shown in FIG. 1A, the analysis engine 307 iscapable of analyzing and processing the recorded uplink data packetstreams to prepare them for playback. In this example, the analysisresults from the analysis engine 307 are stored in the analysis resultsdatabase 5, ready for playback by the playback system 609. In someexamples, the playback system 609 may include a playback server, whichmay be capable of streaming analysis results over a network 12 (e.g.,the Internet). In FIG. 1A, the playback system 609 is shown streaminganalysis results to a plurality of listening stations 11 (each of whichmay include one or more playback software applications running on alocal device, such as a computer). Here, one of the listening stations11 includes headphones 607 and the other listening station 11 includes aspeaker array 608.

As noted above, due to latency issues the playback system 609 may have amore complete set of data packets available for reproduction than wereavailable during the teleconference. In some implementations, there maybe other differences and/or additional differences between theteleconference audio data reproduced by the playback system 609 and theteleconference audio data available for reproduction during theteleconference. For example, a teleconferencing system generally limitsthe data rates for uplink and downlink data packets to a rate that canbe reliably maintained by the network. Furthermore, there is often afinancial incentive to keep the data rate down, because theteleconference service provider may need to provision more expensivenetwork resources if the combined data rate of the system is too high.

In addition to data rate constraints, there may be practical constraintson the number of IP packets that can be reliably handled each second bynetwork components such as switches and routers, and also by softwarecomponents such as the TCP/IP stack in the kernel of a teleconferencingserver's host operating system. Such constraints may have implicationsfor how the data packet streams corresponding to teleconferencing audiodata are encoded and partitioned into IP packets.

A teleconferencing server needs to process data packets and performmixing operations, etc., quickly enough to avoid perceptual qualitydegradation to conference participants, and generally must do so with anupper bound on computational resources. The smaller the computationaloverhead that is required to service a single conference participant,the larger the number of conference participants that can be handled inreal time by a single piece of server equipment. Therefore keeping thecomputational overhead relatively small provides economic benefits toteleconference service providers.

Most teleconference systems are so-called “reservationless” systems.This means that the teleconferencing server does not “know” ahead oftime how many teleconferences it will be expected to host at once, orhow many conference participants will connect to any giventeleconference. At any time during a teleconference, the server hasneither an indication of how many additional conference participants maysubsequently join the teleconference nor an indication of how many ofthe current conference participants may leave the teleconference early.

Moreover, a teleconferencing server will generally not have meetingdynamics information prior to a teleconference regarding of what kind ofhuman interaction is expected to occur during a teleconference. Forexample, it will not be known in advance whether one or more conferenceparticipants will dominate the conversation, and if so, which conferenceparticipant(s). At any instant in time, the teleconferencing server mustdecide what audio to provide in each downlink data packet stream basedonly on what has occurred in the teleconference until that instant.

However, the foregoing set of constraints will generally not apply whenthe analysis engine 307 processes the individual uplink data packetstreams that are stored in the conference recording database 3.Similarly, the foregoing set of constraints will generally not applywhen the playback system 609 is processing and reproducing data from theanalysis results database 5, which has been output from the analysisengine 307.

For example, assuming that analysis and playback occur after theteleconference is complete, the playback system 609 and/or the analysisengine 307 may use information from the entire teleconference recordingin order to determine how best to process, mix and/or render any instantof the teleconference for reproduction during playback. Even if theteleconference recording only corresponds to a portion of theteleconference, data corresponding to that entire portion will beavailable for determining how optimally to mix, render and otherwiseprocess the recorded teleconference audio data (and possibly other data,such as teleconference metadata) for reproduction during playback.

In many implementations, the playback system 609 may be providing audiodata, etc., to a listener who is not trying to interact with those inthe teleconference. Accordingly, the playback system 609 and/or theanalysis engine 307 may have seconds, minutes, hours, days, or even alonger time period in which to analyze and/or process the recordedteleconference audio data and make the teleconference available forplayback. This means that computationally-heavy and/or data-heavyalgorithms, which can only be performed slower than real time on theavailable hardware, may be used by the analysis engine 307 and/or theplayback system 609. Due to these relaxed time constraints, someimplementations may involve queueing up teleconference recordings foranalysis and analyzing them when resources permit (e.g., when analysisof previously-recorded teleconferences is complete or at “off-peak”times of day when electricity or cloud computing resources are lessexpensive or more readily available).

Assuming that analysis and playback occur after a teleconference iscomplete, the analysis engine 307 and the playback system 609 can haveaccess to a complete set of teleconference participation information,e.g., information regarding which conference participants were involvedin the teleconference and the times at which each conference participantjoined and left the teleconference. Similarly, assuming that analysisand playback occur after the teleconference is complete, the analysisengine 307 and the playback system 609 can have access to a complete setof teleconference audio data and any associated metadata from which todetermine (or at least to estimate) when each participant spoke. Thistask may be referred to herein as “speaker diarization.” Based onspeaker diarization information, the analysis engine 307 can determineconversational dynamics data such as which conference participant(s)spoke the most, who spoke to whom, who interrupted whom, how muchdoubletalk (times during which at least two conference participants arespeaking simultaneously) occurred during the teleconference, andpotentially other useful information which the analysis engine 307and/or the playback system 609 can use in order to determine how best tomix and render the conference during playback. Even if theteleconference recording only corresponds to a portion of theteleconference, data corresponding to that entire portion will beavailable for determining teleconference participation information,conversational dynamics data, etc.

The present disclosure includes methods and devices for recording,analyzing and playing back teleconference audio data such that theteleconference audio data presented during playback may be substantiallydifferent from what would have been heard by conference participantsduring the original teleconference and/or what would have been recordedduring the original teleconference by a recording device such as theteleconference recording device 2 shown in FIG. 1A. Variousimplementations disclosed herein make use of one or more of theabove-identified constraint differences between the live teleconferenceand the playback use-cases to produce a better user experience duringplayback. Without loss of generality, we now discuss a number ofspecific implementations and particular methods for recording, analyzingand playing back teleconference audio data such that the playback can beadvantageously different from the original teleconference experience.

FIG. 1B is a block diagram that shows examples of components of anapparatus capable of implementing various aspects of this disclosure.The types and numbers of components shown in FIG. 1B are merely shown byway of example. Alternative implementations may include more, fewerand/or different components. The apparatus 10 may, for example, be aninstance of a teleconferencing apparatus 200. In some examples, theapparatus 10 may be a component of another device. For example, in someimplementations the apparatus 10 may be a component of ateleconferencing apparatus 200, e.g., a line card.

In this example, the apparatus 10 includes an interface system 105 and acontrol system 110. The interface system 105 may include one or morenetwork interfaces, one or more interfaces between the control system110 and a memory system and/or one or more an external device interfaces(such as one or more universal serial bus (USB) interfaces). The controlsystem 110 may, for example, include a general purpose single- ormulti-chip processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) or other programmable logic device, discrete gate or transistorlogic, and/or discrete hardware components. In some implementations, thecontrol system 110 may be capable of providing teleconference serverfunctionality.

FIG. 1C is a flow diagram that outlines one example of a method that maybe performed by the apparatus of FIG. 1B. The blocks of method 150, likeother methods described herein, are not necessarily performed in theorder indicated. Moreover, such methods may include more or fewer blocksthan shown and/or described.

In this implementation, block 155 involves receiving teleconferenceaudio data during a teleconference, via an interface system. Forexample, the teleconference audio data may be received by the controlsystem 110 via the interface system 105 in block 155. In this example,the teleconference audio data includes a plurality of individual uplinkdata packet streams, such as the uplink data packet streams 7 shown inFIG. 1A. Accordingly, each uplink data packet stream corresponds to atelephone endpoint used by one or more conference participants.

In this example, block 160 involves sending to a memory system, via theinterface system, the teleconference audio data as individual uplinkdata packet streams. Accordingly, instead of being recorded as mixedaudio data received as one of the downlink data packet streams 8 shownin FIG. 1A, such as the downlink data packet stream 8 that is recordedby the teleconference recording device 2, the packets received via eachof the uplink data packet streams 7 are recorded and stored asindividual uplink data packet streams.

However, in some examples at least one of the uplink data packet streamsmay correspond to multiple conference participants. For example, block155 may involve receiving such an uplink data packet stream from aspatial speakerphone used by multiple conference participants.Accordingly, in some instances the corresponding uplink data packetstream may include spatial information regarding each of the multipleparticipants.

In some implementations, the individual uplink data packet streamsreceived in block 155 may be individual encoded uplink data packetstreams. In such implementations, block 160 may involve sending theteleconference audio data to the memory system as individual encodeduplink data packet streams.

As noted above, in some examples the interface system 105 may include anetwork interface. In some such examples, block 160 may involve sendingthe teleconference audio data to a memory system of another device viathe network interface. However, in some implementations the apparatus 10may include at least part of the memory system. The interface system 105may include an interface between the control system and at least part ofthe memory system. In some such implementations, block 160 may involvesending the teleconference audio data to a memory system of theapparatus 10.

Due at least in part to the teleconferencing latency issues describedabove, at least one of the uplink data packet streams may include atleast one data packet that was received after a mouth-to-ear latencytime threshold of the teleconference and was therefore not used forreproducing audio data during the teleconference. The mouth-to-earlatency time threshold may differ from implementation to implementation,but in many implementations the mouth-to-ear latency time threshold maybe 150 ms or less. In some examples, the mouth-to-ear latency timethreshold may be greater than or equal to 100 ms.

In some implementations, the control system 110 may be capable ofdetermining that a late data packet of an incomplete uplink data packetstream has not been received from a telephone endpoint within a latepacket time threshold. In some implementations, the late packet timethreshold may be greater than or equal to a mouth-to-ear latency timethreshold of the teleconference. For example, in some implementationsthe late packet time threshold may be greater than or equal to 200 ms,400 ms, 500 ms, 1 second or more. In some examples, the control system110 may be capable of determining that a data packet of an incompleteuplink data packet stream has not been received from a telephoneendpoint within a missing packet time threshold, greater than the latepacket time threshold. In some implementations, the control system 110may be capable of transmitting a request to the telephone endpoint, viathe interface system 105, to re-send the missing data packet. Thecontrol system 110 may be capable of receiving the missing data packetand of adding the missing data packet to the incomplete uplink datapacket stream.

FIG. 2 shows additional examples of components of a teleconferencingsystem. The types and numbers of components shown in FIG. 2 are merelyshown by way of example. Alternative implementations may include more,fewer and/or different components. In this example, the teleconferencingapparatus 200 includes a VoIP teleconferencing bridge. In this example,there are five telephone endpoints being used by the conferenceparticipants, including two headset endpoints 206, a spatialspeakerphone endpoint 207, and two PSTN endpoints 208. The spatialspeakerphone endpoint 207 may be capable of providing spatialinformation corresponding to positions of each of multiple conferenceparticipants. Here, a PSTN bridge 209 forms a gateway between an IPnetwork and the PSTN endpoints 208, converting PSTN signals to IP datapacket streams and vice versa.

FIG. 2A shows additional examples of components of a teleconferencingsystem. The types and numbers of components shown in FIG. 2A are merelyshown by way of example. Alternative implementations may include more,fewer and/or different components. In this example, the teleconferencingapparatus 200 includes a VoIP teleconferencing bridge. In this example,there are five telephone endpoints being used by the conferenceparticipants, including two headset endpoints 206, a spatialspeakerphone endpoint 207, and two PSTN endpoints 208. The spatialspeakerphone endpoint 207 may be capable of providing spatialinformation corresponding to positions of each of multiple conferenceparticipants. Here, a PSTN bridge 209 forms a gateway between an IPnetwork and the PSTN endpoints 208, converting PSTN signals to IP datapacket streams and vice versa.

In FIG. 2A, uplink data packet streams 201A-205A, each corresponding toone of the five telephone endpoints, are being received by theteleconferencing apparatus 200. In some instances, there may be multipleconference participants participating in the teleconference via thespatial speakerphone endpoint 207. If so, the uplink data packet stream203A may include audio data and spatial information for each of themultiple conference participants.

In some implementations, each of the uplink data packet streams201A-205A may include a sequence number for each data packet, as well asa data packet payload. In some examples, each of the uplink data packetstreams 201A-205A may include a talkspurt number corresponding with eachtalkspurt included in an uplink data packet stream. For example, eachtelephone endpoint (or a device associated with a telephone endpointsuch as the PSTN bridge 209) may include a voice activity detector thatis capable detecting instances of speech and non-speech. The telephoneendpoint or associated device may include a talkspurt number in one ormore data packets of an uplink data packet stream corresponding withsuch instances of speech, and may increment the talkspurt number eachtime that the voice activity detector determines that speech hasrecommenced after a period of non-speech. In some implementations, thetalkspurt number may be a single bit that toggles between 1 and 0 at thestart of each talkspurt.

In this example, the teleconferencing apparatus 200 assigns a “receive”timestamp to each received uplink data packet. Here, theteleconferencing apparatus 200 sends packet trace files 201B-205B, eachof which corresponds to one of the uplink data packet streams 201A-205A,to the conference recording database 3. In this implementation, thepacket trace files 201B-205B include a receive timestamp for eachreceived uplink data packet, as well as the received sequence number,talkspurt number and data packet payloads.

In this example, the teleconferencing apparatus 200 also sendsconference metadata 210 to the conference recording database 3. Theconference metadata 210 may, for example, include data regardingindividual conference participants, such as conference participant name,conference participant location, etc. The conference metadata 210 mayindicate associations between individual conference participants and oneof the packet trace files 201B-205B. In some implementations, the packettrace files 201B-205B and the conference metadata 210 may together formone teleconference recording in the conference recording database 3.

FIG. 2B shows examples of packet trace files and conference metadata. Inthis example, the conference metadata 210 and the packet trace files201B-204B have data structures that are represented as tables thatinclude four columns, also referred to herein as fields. The particulardata structures shown in FIG. 2B are merely made by way of example;other examples may include more or fewer fields. As described elsewhereherein, in some implementations the conference metadata 210 may includeother types of information that are not shown in FIG. 2B.

In this example, the conference metadata 210 data structure includes aconference participant name field 212, a connection time field 214(indicating when the corresponding conference participants joined theconference), a disconnection time field 216 (indicating when thecorresponding conference participants left the conference) and a packettrace file field 218. It may be seen in this example that the sameconference participant may be listed multiple times in the conferencemetadata 210 data structure, once for every time he or she joins orrejoins the conference. The packet trace file field 218 includesinformation for identifying a corresponding packet trace file.

Accordingly, the conference metadata 210 provides a summary of someevents of a conference, including who participated, for how long, etc.In some implementations, the conference metadata 210 may include otherinformation, such as the endpoint type (e.g., headset, mobile device,speaker phone, etc.).

In this example, each of the packet trace files 201B-204B also includesfour fields, each field corresponding to a different type ofinformation. Here, each of the packet trace files 201B-204B includes areceived time field 222, a sequence number field 224, a talkspurtidentification field 226 and a payload data field 228. The sequencenumbers and talkspurt numbers, which may be included in packet payloads,enable the payloads to be arranged in the correct order. In thisexample, each instance of payload data indicated by the payload datafield 228 corresponds to the remainder of the payload of a packet afterthe sequence number and talkspurt number have been removed, includingthe audio data corresponding to the corresponding conferenceparticipant. Each of the packet trace files 201B-204B may, for example,contain the payload data of packets originating from an endpoint such asthose shown in FIG. 2A. One packet trace file may include payload datafrom a large number of packets.

Although not shown in FIG. 2B, the conference metadata 210 correspondsto a particular conference. Accordingly, the metadata and packet tracefiles 201B-204B for a conference, including the payload data, may bestored for later retrieval according to, e.g., a conference code.

The packet trace files 201B-204B and the conference metadata 210 maychange over the duration of a conference, as more information is added.According to some implementations, such changes may happen locally, withthe final packet trace files and the conference metadata 210 being sentto the conference recording database 3 after the conference has ended.Alternatively, or additionally, the packet trace files 201B-204B and/orthe conference metadata 210 can be created, and then updated, on theconference recording database 3.

FIG. 3A is a block diagram that shows examples of components of anapparatus capable of implementing various aspects of this disclosure.The types and numbers of components shown in FIG. 3A are merely shown byway of example. Alternative implementations may include more, fewerand/or different components. The apparatus 300 may, for example, be aninstance of an analysis engine 307. In some examples, the apparatus 300may be a component of another device. For example, in someimplementations the apparatus 300 may be a component of an analysisengine 307, e.g., an uplink analysis module described elsewhere herein.

In this example, the apparatus 300 includes an interface system 325 anda control system 330. The interface system 325 may include one or morenetwork interfaces, one or more interfaces between the control system330 and a memory system and/or one or more an external device interfaces(such as one or more universal serial bus (USB) interfaces). The controlsystem 330 may, for example, include a general purpose single- ormulti-chip processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) or other programmable logic device, discrete gate or transistorlogic, and/or discrete hardware components.

FIG. 3B is a flow diagram that outlines one example of a method that maybe performed by the apparatus of FIG. 3A. The blocks of method 350, likeother methods described herein, are not necessarily performed in theorder indicated. Moreover, such methods may include more or fewer blocksthan shown and/or described.

In this implementation, block 355 involves receiving previously storedaudio data, also referred to herein as recorded audio data, for ateleconference, via an interface system. For example, the recorded audiodata may be received by the control system 330 via the interface system325 in block 355. In this example, the recorded audio data includes atleast one individual uplink data packet stream corresponding to atelephone endpoint used by one or more conference participants.

Here, the received individual uplink data packet stream includestimestamp data corresponding to data packets of the individual uplinkdata packet stream. As noted above, in some implementations ateleconferencing apparatus 200 may assign a receive timestamp to eachreceived uplink data packet. A teleconferencing apparatus 200 may store,or may cause to be stored, time-stamped data packets in the order theywere received by the teleconference server 200. Accordingly, in someimplementations block 355 may involve receiving the recorded audio data,including the individual uplink data packet stream that includestimestamp data, from a conference recording database 3 such as thatshown in FIG. 1A, above.

In this example, block 360 involves analyzing timestamp data of datapackets in the individual uplink data packet stream. Here, the analyzingprocess of block 360 involves determining whether the individual uplinkdata packet stream includes at least one out-of-order data packet. Inthis implementation, if the individual uplink data packet streamincludes at least one out-of-order data packet, the individual uplinkdata packet stream will be re-ordered according to the timestamp data,in block 365.

In some implementations, at least one data packet of the individualuplink data packet stream may have been received after a mouth-to-earlatency time threshold of the teleconference. If so, the individualuplink data packet stream includes data packets that would not have beenavailable for including in downlink data packet streams for reproductionto conference participants or for recording at a telephone endpoint.Data packets received after the mouth-to-ear latency time threshold mayor may not have been received out of order, depending on the particularcircumstance.

The control system 330 of FIG. 3A may be capable of various otherfunctionality. For example, the control system 330 may be capable ofreceiving, via the interface system 325, teleconference metadata and ofindexing the individual uplink data packet stream based, at least inpart, on the teleconference metadata.

The recorded audio data received by the control system 330 may include aplurality of individual encoded uplink data packet streams, each of theindividual encoded uplink data packet streams corresponding to atelephone endpoint used by one or more conference participants. In someimplementations, as described in more detail below, the control system330 may include a joint analysis module capable of analyzing a pluralityof individual uplink data packet streams. The joint analysis module maybe capable of determining conversational dynamics data, such as dataindicating the frequency and duration of conference participant speech,data indicating instances of conference participant doubletalk duringwhich at least two conference participants are speaking simultaneouslyand/or data indicating instances of conference participantconversations.

The control system 330 may be capable of decoding each of the pluralityof individual encoded uplink data packet streams. In someimplementations, the control system 330 may be capable of providing oneor more decoded uplink data packet streams to a speech recognitionmodule capable of recognizing speech and generating speech recognitionresults data. The speech recognition module may be capable of providingthe speech recognition results data to the joint analysis module. Insome implementations, the joint analysis module may be capable ofidentifying keywords in the speech recognition results data and ofindexing keyword locations.

In some implementations, the control system 330 may be capable ofproviding one or more decoded uplink data packet streams to a speakerdiarization module. The speaker diarization module may be capable ofidentifying speech of each of multiple conference participants in anindividual decoded uplink data packet stream. The speaker diarizationmodule may be capable of generating a speaker diary indicating times atwhich each of the multiple conference participants were speaking and ofproviding the speaker diary to the joint analysis module. In someimplementations, the control system 330 may be capable of providing aplurality of individual decoded uplink data packet streams to the jointanalysis module.

FIG. 3C shows additional examples of components of a teleconferencingsystem. The types and numbers of components shown in FIG. 3C are merelyshown by way of example. Alternative implementations may include more,fewer and/or different components. In this implementation, various filesfrom a conference recording database 3 and information from a conferencedatabase 308 are being received by an analysis engine 307. The analysisengine 307 and its components may be implemented via hardware, viasoftware stored on non-transitory media, via firmware and/or bycombinations thereof. The information from the conference database 308may, for example, include information regarding which conferencerecordings exist, regarding who has permission to listen to and/ormodify each conference recording, regarding which conferences werescheduled and/or regarding who was invited to each conference, etc.

In this example, the analysis engine 307 is receiving packet trace files201B-205B from the conference recording database 3, each of whichcorresponds to one of the uplink data packet streams 201A-205A that hadpreviously been received by the teleconferencing apparatus 200. Thepacket trace files 201B-205B may, for example, include a receivetimestamp for each received uplink data packet, as well as a receivedsequence number, talkspurt number and data packet payloads. In thisexample, each of the packet trace files 201B-205B is provided to aseparate one of the uplink analysis modules 301-305 for processing. Insome implementations, the uplink analysis modules 301-305 may be capableof re-ordering data packets of a packet trace file, e.g., as describedabove with reference to FIG. 3B. Some additional examples of uplinkanalysis module functionality are described below with reference to FIG.4.

In this example, each of the uplink analysis modules 301-305 outputs acorresponding one of the per-uplink analysis results 301C-305C. In someimplementations, the per-uplink analysis results 301C-305C may be usedby the playback system 609 for playback and visualization. Some examplesare described below with reference to FIG. 6.

Here, each of the uplink analysis modules 301-305 also provides outputto the joint analysis module 306. The joint analysis module 306 may becapable of analyzing data corresponding to a plurality of individualuplink data packet streams.

In some examples, the joint analysis module 306 may be capable ofanalyzing conversational dynamics and determining conversationaldynamics data. These and other examples of joint analysis modulefunctionality are described in more detail below with reference to FIG.5.

In this example, the joint analysis module 306 outputs meeting overviewinformation 311, which may include the time of a conference, names ofparticipants, etc. In some implementations, the meeting overviewinformation 311 may include conversational dynamics data. Here, thejoint analysis module 306 also outputs segment and word cloud data 309and a search index 310, both of which are described below with referenceto FIG. 5.

Here, the analysis engine 307 is also receiving conference metadata 210.As noted elsewhere herein, the conference metadata 210 may include dataregarding individual conference participants, such as conferenceparticipant name and/or conference participant location, associationsbetween individual conference participants and one of the packet tracefiles 201B-205B, etc. In this example, the conference metadata 210 areprovided to the joint analysis module 306.

FIG. 4 shows examples of components of an uplink analysis module. Theuplink analysis module 301 and its components may be implemented viahardware, via software stored on non-transitory media, via firmwareand/or by combinations thereof. The types and numbers of componentsshown in FIG. 4 are merely shown by way of example. Alternativeimplementations may include more, fewer and/or different components.

In this implementation, the uplink analysis module 301 is shownreceiving the packet trace file 201B. Here, the packet trace file 201B,corresponding to an individual uplink data packet stream, is receivedand processed by the packet stream normalization module 402. In thisexample, the packet stream normalization module 402 is capable ofanalyzing sequence number data of data packets in the packet trace file201B and determining whether the individual uplink data packet streamincludes at least one out-of-order data packet. If the packet streamnormalization module 402 determines that the individual uplink datapacket stream includes at least one out-of-order data packet, in thisexample the packet stream normalization module 402 will re-order theindividual uplink data packet stream according to the sequence numbers.

In this implementation, the packet stream normalization module 402outputs an ordered playback stream 401B as one component of the uplinkanalysis results 301C output by the uplink analysis module 301. In someimplementations, the packet stream normalization module 402 may includea playback timestamp and a data packet payload corresponding to eachdata packet of the ordered playback stream 401B. Here, the orderedplayback stream 401B includes encoded data, but in alternativeimplementations the ordered playback stream 401B may include decodeddata or transcoded data. In this example, the playback stream index401A, output by the packet stream indexing module 403, is anothercomponent of the uplink analysis results 301C. The playback stream index401A may facilitate random access playback by the playback system 609.

The packet stream indexing module 403 may, for example, determineinstances of talkspurts of conference participants (e.g., according totalkspurt numbers of the input uplink packet trace) and includecorresponding index information in the playback stream index 401A, inorder to facilitate random access playback of the conference participanttalkspurts by the playback system 609. In some implementations, thepacket stream indexing module 403 may be capable of indexing accordingto time. For example, in some examples the packet stream indexing module403 may be capable of forming a packet stream index that indicates thebyte offset within the playback stream of the encoded audio for acorresponding playback time. In some such implementations, duringplayback the playback system 609 may look up a particular time in thepacket stream index (for example, according to a time granularity, suchas a 10-second granularity) and the packet stream index may indicate abyte offset within the playback stream of the encoded audio for thatplayback time. This is potentially useful because the encoded audio mayhave a variable bit rate or because there may be no packets when thereis silence (so called “DTX” or “discontinuous transmission”). In eithercase, the packet stream index can facilitate fast seeking during aplayback process, at least in part because there may often be anon-linear relationship between time and byte offset within the playbackstream.

In the example shown in FIG. 4, the decoding module 404 also receives anordered playback stream 401B from the packet stream normalization module402. In this implementation, the decoding module 404 decodes the encodedordered playback stream 401B and provides the automatic speechrecognition module 405, the visualization analysis module 406 and thespeaker diarization module 407 with a decoded playback stream. In someexamples, the decoded playback stream may be a pulse code modulation(PCM) stream.

According to some implementations, the decoding module 404 and/or theplayback system 609 may apply a different decoding process from thedecoding process used during the original teleconference. Due to time,computational and/or bandwidth constraints, the same packet of audio maybe decoded in low fidelity with minimal computational requirementsduring the teleconference, but decoded in higher fidelity with highercomputational requirements by the decoding module 404. Higher-fidelitydecoding by the decoding module 404 may, for example, involve decodingto a higher sample rate, switching on spectral bandwidth replication(SBR) for better perceptual results, running more iterations of aniterative decoding process, etc.

In the example shown in FIG. 4, the automatic speech recognition module405 analyzes audio data in the decoded playback stream provided by thedecoding module 404 to determine spoken words in the teleconferenceportion corresponding to the decoded playback stream. The automaticspeech recognition module 405 outputs speech recognition results 401F tothe joint analysis module 306.

In this example, the visualization analysis module 406 analyzes audiodata in the decoded playback stream to determine the occurrences oftalkspurts, the amplitude of the talkspurts and/or the frequency contentof the talkspurts, etc., and outputs visualization data 401D. Thevisualization data 401D may, for example, provide information regardingwaveforms that the playback system 609 may display when theteleconference is played back.

In this implementation, the speaker diarization module 407 analyzesaudio data in the decoded playback stream to identify and recordoccurrences of speech from one or more conference participants,depending on whether a single conference participant or multipleconference participants were using the same telephone endpoint thatcorresponds to the input uplink packet trace 201B. The speakerdiarization module 407 outputs speaker diary 401E which, along with thevisualization data 401D, is included as part of the uplink analysisresults 301C output by the analysis engine 307 (see FIG. 3C). Inessence, the speaker diary 401E indicates which conferenceparticipant(s) spoke and when the conference participant(s) spoke.

The uplink analysis results 301C, together with the speech recognitionresults 401F, are included in the uplink analysis results available forjoint analysis 401 provided to the joint analysis module 306. Each of aplurality of uplink analysis modules may output an instance of theuplink analysis results available for joint analysis to the jointanalysis module 306.

FIG. 5 shows examples of components of a joint analysis module. Thejoint analysis module 306 and its components may be implemented viahardware, via software stored on non-transitory media, via firmwareand/or by combinations thereof. The types and numbers of componentsshown in FIG. 5 are merely shown by way of example. Alternativeimplementations may include more, fewer and/or different components.

In this example, each of the uplink analysis modules 301-305 shown inFIG. 3C has output a corresponding one of the uplink analysis resultsavailable for joint analysis 401-405, all of which are shown in FIG. 5as being received by the joint analysis module 306. In thisimplementation, the speech recognition results 401F-405F, one of whichis from each of the uplink analysis results available for joint analysis401-405, are provided to the keyword spotting and indexing module 505and to the topic analysis module 525. In this example, the speechrecognition results 401F-405F correspond to all conference participantsof a particular teleconference. The speech recognition results 401F-405Fmay, for example, be text files.

In this example, the keyword spotting and indexing module 505 is capableof analyzing the speech recognition results 401F-405F, of identifyingfrequently-occurring words that were spoken by all conferenceparticipants during the teleconference and of indexing occurrences ofthe frequently-occurring words. In some implementations, the keywordspotting and indexing module 505 may determine and record the number ofinstances of each keyword. In this example, the keyword spotting andindexing module 505 outputs the search index 310.

In the example shown in FIG. 5, the conversational dynamics analysismodule 510 receives the speaker diaries 401E-405E, one of which is fromeach of the uplink analysis results available for joint analysis401-405. The conversational dynamics analysis module 510 may be capableof determining conversational dynamics data, such as data indicating thefrequency and duration of conference participant speech, data indicatinginstances of conference participant “doubletalk” during which at leasttwo conference participants are speaking simultaneously, data indicatinginstances of conference participant conversations and/or data indicatinginstances of one conference participant interrupting one or more otherconference participants, etc.

In this example, the conversational dynamics analysis module 510 outputsconversational dynamics data files 515 a-515 d, each of whichcorresponds to a different timescale. For example, the conversationaldynamics data file 515 a may correspond to a timescale wherein segmentsof the conference (presentation, discussion, etc.) are approximately 1minute long, the conversational dynamics data file 515 b may correspondto a timescale wherein segments of the conference are approximately 3minutes long, the conversational dynamics data file 515 c may correspondto a timescale wherein segments of the conference are approximately 5minutes long, and the conversational dynamics data file 515 d maycorrespond to a timescale wherein segments of the conference areapproximately 7 minutes long or longer. In other implementations, theconversational dynamics analysis module 510 may output more or fewer ofthe conversational dynamics data files 515. In this example, theconversational dynamics data files 515 a-515 d are output only to thetopic analysis module 525, but in other implementations theconversational dynamics data files 515 a-515 d may be output to one ormore other modules and/or output from the entire analysis engine 307.Accordingly, in some implementations the conversational dynamics datafiles 515 a-515 d may be made available to the playback system 609.

In some implementations, the topic analysis module 525 may be capable ofanalyzing the speech recognition results 401F-405F and of identifyingpotential conference topics. In some examples, as here, the topicanalysis module 525 may receive and process the conference metadata 210.Various implementations of the topic analysis module 525 are describedin detail below. In this example, the topic analysis module 525 outputsthe segment and word cloud data 309, which may include with topicinformation for each of a plurality of conversation segments and/ortopic information for each of a plurality of time intervals.

In the example shown in FIG. 5, the joint analysis module includes anoverview module 520. In this implementation, the overview module 520receives the conference metadata 210 as well as data from the conferencedatabase 308. The conference metadata 210 may include data regardingindividual conference participants, such as conference participant nameand conference participant location, data indicating the time and dateof a conference, etc. The conference metadata 210 may indicateassociations between individual conference participants and telephoneendpoints. For example, the conference metadata 210 may indicateassociations between individual conference participants and one of theanalysis results 301C-305C output by the analysis engine (see FIG. 3C).The conference database 308 may provide data to the overview module 520regarding which conferences were scheduled, regarding meeting topicsand/or regarding who was invited to each conference, etc. In thisexample, the overview module 520 outputs meeting the overviewinformation 311, which may include a summary of the conference metadata210 and of the data from the conference database 308.

In some implementations, the analysis engine 307 and/or other componentsof the teleconferencing system 100 may be capable of otherfunctionality. For example, in some implementations the analysis engine307, the playback system 609 or another component of theteleconferencing system 100 may be capable of assigning virtualconference participant positions in a virtual acoustic space based, atleast in part, on conversational dynamics data. In some examples, theconversational dynamics data may be based on an entire conference.

FIG. 6 shows examples of components of a playback system and associatedequipment. The playback system 609 and its components may be implementedvia hardware, via software stored on non-transitory media, via firmwareand/or by combinations thereof. The types and numbers of componentsshown in FIG. 6 are merely shown by way of example. Alternativeimplementations may include more, fewer and/or different components.

In this example, the playback system 609 is receiving data correspondingto a teleconference that included three telephone endpoints, instead ofa teleconference that included five telephone endpoints as describedabove. Accordingly, the playback system 609 is shown receiving analysisresults 301C-303C, as well as the segment and word cloud data 309, thesearch index 310 and the meeting overview information 311.

In this implementation, the playback system 609 includes a plurality ofdecoding units 601A-603A. Here, decoding units 601A-603A are receivingordered playback streams 401B-403B, one from each of the analysisresults 301C-303C. In some examples, the playback system 609 may invokeone decoding unit per playback stream, so the number of decoding unitsmay change depending on the number of playback streams received.

According to some implementations, the decoding units 601A-603A mayapply a different decoding process from the decoding process used duringthe original teleconference. As noted elsewhere herein, during theoriginal teleconference audio data may be decoded in low fidelity withminimal computational requirements, due to time, computational and/orbandwidth constraints. However, the ordered playback streams 401B-403Bmay be decoded in higher fidelity, potentially with higher computationalrequirements, by the decoding units 601A-603A. Higher-fidelity decodingby the decoding units 601A-603A may, for example, involve decoding to ahigher sample rate, switching on spectral bandwidth replication (SBR)for better perceptual results, running more iterations of an iterativedecoding process, etc.

In this example, a decoded playback stream is provided by each of thedecoding units 601A-603A to a corresponding one of the post-processingmodules 601B-603B. As discussed in more detail below, in someimplementations the post-processing modules 601B-603B may be capable ofone or more types of processing to speed up the playback of the orderedplayback streams 401B-403B. In some such examples, the post-processingmodules 601B-603B may be capable of removing silent portions from theordered playback streams 401B-403B, overlapping portions of the orderedplayback streams 401B-403B that were not previously overlapping,changing the amount of overlap of previously overlapping portions of theordered playback streams 401B-403B and/or other processing to speed upthe playback of the ordered playback streams 401B-403B.

In this implementation, a mixing and rendering module 604 receivesoutput from the post-processing modules 601B-603B. Here, the mixing andrendering module 604 is capable of mixing the individual playbackstreams received from the post-processing modules 601B-603B andrendering the resulting playback audio data for reproduction by aspeaker system, such as the headphones 607 and/or the speaker array 608.In some examples, the mixing and rendering module 604 may provide theplayback audio data directly to a speaker system, whereas in otherimplementations the mixing and rendering module 604 may provide theplayback audio data to another device, such as the display device 610,which may be capable of communication with the speaker system. In someimplementations, the mixing and rendering module 604 may be capable ofrendering the mixed audio data according to spatial informationdetermined by the analysis engine 307. For example, the mixing andrendering module 604 may be capable of rendering the mixed audio datafor each conference participant to an assigned virtual conferenceparticipant position in a virtual acoustic space based on such spatialinformation. In some alternative implementations, the mixing andrendering module 604 also may be capable of determining such spatialinformation. In some instances, the mixing and rendering module 604 mayrender teleconference audio data according to different spatialparameters than were used for rendering during the originalteleconference.

In some implementations, some functionality of the playback system 609may be provided, at least in part, according to “cloud-based” systems.For example, in some implementations the playback system 609 may becapable of communicating with one or more other devices, such as one ormore servers, via a network. In the example shown in FIG. 6, theplayback system 609 is shown communicating with an optional playbackcontrol server 650 and an optional rendering server 660, via one or morenetwork interfaces (not shown). According to some such implementations,at least some of the functionality that could, in other implementations,be performed by the mixing and rendering module 604 may be performed bythe rendering server 660. Similarly, in some implementations at leastsome of the functionality that could, in other implementations, beperformed by the playback control module 605 may be performed by theplayback control server 650. In some implementations, the functionalityof the decoding units 601A-603A and/or the post-processing modules601B-603B may be performed by one or more servers. According to someexamples, the functionality of the entire playback system 609 may beimplemented by one or more servers. The results may be provided to aclient device, such as the display device 610, for playback.

In this example, a playback control module 605 is receiving the playbackstream indices 401A-403A, one from each of the analysis results301C-303C. Although not shown in FIG. 6, the playback control module 605also may receive other information from the analysis results 301C-303C,as well as the segment and word cloud data 309, the search index 310 andthe meeting overview information 311. The playback control module 605may be capable of controlling a playback process (including reproductionof audio data from the mixing and rendering module 604) based, at leastin part, on user input (which may be received via the display device 610in this example), on the analysis results 301C-303C, on the segment andword cloud data 309, the search index 310 and/or on the meeting overviewinformation 311.

In this example, the display device 610 is shown providing a graphicaluser interface 606, which may be used for interacting with playbackcontrol module 605 to control playback of audio data. The display device610 may, for example, be a laptop computer, a tablet computer, a smartphone or another type of device. In some implementations, a user may beable to interact with the graphical user interface 606 via a userinterface system of the display device 610, e.g., by touching anoverlying touch screen, via interaction with an associated keyboardand/or mouse, by voice command via a microphone and associated softwareof the display device 610, etc.

In the example shown in FIG. 6, each row 615 of the graphical userinterface 606 corresponds to a particular conference participant. Inthis implementation, the graphical user interface 606 indicatesconference participant information 620, which may include a conferenceparticipant name, conference participant location, conferenceparticipant photograph, etc. In this example, waveforms 625,corresponding to instances of the speech of each conference participant,are also shown the graphical user interface 606. The display device 610may, for example, display the waveforms 625 according to instructionsfrom playback control module 605. Such instructions may, for example bebased on visualization data 410D-403D that is included in the analysisresults 301C-303C. In some examples, a user may be able to change thescale of the graphical user interface 606, according to a desired timeinterval of the conference to be represented. For example, a user may beable to “zoom in” or enlarge at least a portion of the graphical userinterface 606 to show a smaller time interval or “zoom out” at least aportion of the graphical user interface 606 to show a larger timeinterval. According to some such examples, the playback control module605 may access a different instance of the conversational dynamics datafiles 515, corresponding with the changed time interval.

In some implementations a user may be able to control the reproductionof audio data not only according to typical commands such as pause,play, etc., but also according to additional capabilities based on aricher set of associated data and metadata. For example, in someimplementations a user may be able to select for playback only thespeech of a selected conference participant. In some examples, a usermay be able to select for playback only those portions of a conferencein which a particular keyword and/or a particular topic is beingdiscussed.

In some implementations the graphical user interface 606 may display oneor more word clouds based, at least in part, on the segment and wordcloud data 309. In some implementations the displayed word clouds may bebased, at least in part, on user input and/or on a particular portion ofthe conference that is being played back at a particular time. Variousexamples are disclosed herein.

Although various examples of audio data processing have been describedabove primarily in the teleconferencing context, the present disclosureis more broadly applicable to other known audio data processingcontexts, such as processing audio data corresponding to in-personconferences. Such in-person conferences may, for example, includeacademic and/or professional conferences, doctor/client visits, personaldiarization (e.g., via a portable recording device such as a wearablerecording device), etc.

FIG. 7 shows an example of an in-person conference implementation. Thetypes and numbers of components shown in FIG. 7 are merely shown by wayof example. Alternative implementations may include more, fewer and/ordifferent components. In this example, a conference location 700includes a conference participant table 705 and a listener seating area710. In this implementation, microphones 715 a-715 d are positioned onthe conference participant table 705. Accordingly, the conferenceparticipant table 705 is set up such that each of four conferenceparticipants will have his or her separate microphone.

In this implementation, each of the cables 712 a-712 d convey anindividual stream of audio data from a corresponding one of themicrophones 715 a-715 d to a recording device 720, which is locatedunder the conference participant table 705 in this instance. Inalternative examples, the microphones 715 a-715 d may communicate withthe recording device 720 via wireless interfaces, such that the cables712 a-712 d are not required. Some implementations of the conferencelocation 700 may include additional microphones 715, which may or maynot be wireless microphones, for use in the listener seating area 710and/or use in the area between the listener seating area 710 and theconference participant table 705.

In this example, the recording device 720 does not mix the individualstreams of audio data, but instead records each individual stream ofaudio data separately. In some implementations, either the recordingdevice 720 or each of the microphones 715 a-715 d may include ananalog-to-digital converter, such that the streams of audio data fromthe microphones 715 a-715 d may be recorded by the recording device 720as individual streams of digital audio data.

The microphones 715 a-715 d may sometimes be referred to as examples of“endpoints,” because they are analogous to the telephone endpointsdiscussed above in the teleconferencing context. Accordingly, theimplementation shown in FIG. 7 provides another example in which theaudio data for each of multiple endpoints, represented by themicrophones 715 a-715 d in this example, will be recorded separately.

In alternative implementations, the conference participant table 705 mayinclude a microphone array, such as a soundfield microphone. Thesoundfield microphone may, for example, be capable of producingAmbisonic signals in A-format or B-format (such as the Core SoundTetraMic™), a Zoom H4n™, an MH Acoustics Eigenmike™, or a spatialspeakerphone such as a Dolby Conference Phone™. The microphone array maybe referred to herein as a single endpoint. However, audio data fromsuch a single endpoint may correspond to multiple conferenceparticipants. In some implementations, the microphone array may becapable of detecting spatial information for each conference participantand of including the spatial information for each conference participantin the audio data provided to the recording device 720.

In view of the foregoing, the present disclosure encompasses variousimplementations in which audio data for conference involving a pluralityof conference participants may be recorded. In some implementations, theconference may be a teleconference whereas in other implementations theconference may be an in-person conference. In various examples, theaudio data for each of multiple endpoints may be recorded separately.Alternatively, or additionally, recorded audio data from a singleendpoint may correspond to multiple conference participants and mayinclude spatial information for each conference participant.

Various disclosed implementations involve processing and/or playback ofdata recorded in either or both of the foregoing manners. Some suchimplementations involve determining a virtual conference participantposition for each of the conference participants in a virtual acousticspace. Positions within the virtual acoustic space may be determinedrelative to a virtual listener's head. In some examples, the virtualconference participant positions may be determined, at least in part,according to the psychophysics of human sound localization, according tospatial parameters that affect speech intelligibility and/or accordingto empirical data that reveals what talker locations listeners havefound to be relatively more or less objectionable, given theconversational dynamics of a conference.

In some implementations, audio data corresponding to an entireconference, or at least a substantial portion of a teleconference, maybe available for determining the virtual conference participantpositions. Accordingly, a complete or substantially complete set ofconversational dynamics data for the conference may be determined. Insome examples, the virtual conference participant positions may bedetermined at least in part, according to a complete or substantiallycomplete set of conversational dynamics data for a conference.

For example, the conversational dynamics data may include dataindicating the frequency and duration of conference participant speech.It has been found in listening exercises that many people object to aprimary speaker in a conference being rendered to a virtual positionbehind, or beside the listener. When listening to a long section ofspeech from one talker (e.g., during a business presentation) manylisteners report that they would like a sound source corresponding tothe talker to be positioned in front of the listener, just as if thelistener were present in a lecture or seminar. For long sections ofspeech from one talker, positioning behind or beside often evokes thecomment that it seems unnatural, or, in some cases, that the listener'spersonal space is being invaded. Accordingly, the frequency and durationof conference participant speech may be useful input to a process ofassigning and/or rendering virtual conference participant positions fora playback of an associated conference recording.

In some implementations, the conversational dynamics data may includedata indicating instances of conference participant conversations. Ithas been found that rendering conference participants engaged in aconversation to substantially different virtual conference participantpositions can improve a listener's ability to distinguish whichconference participant is talking at any given time and can improve thelistener's ability to understand what each conference participant issaying.

The conversational dynamics data may include instances of so-called“doubletalk” during which at least two conference participants arespeaking simultaneously. It has been found that rendering conferenceparticipants engaged in doubletalk to substantially different virtualconference participant positions can provide the listener an advantage,as compared with rendering conference participants engaged in doubletalkto the same virtual position. Such differentiated positioning providesthe listener with better cues to selectively attend to one of theconference participants engaged in doubletalk and/or to understand whateach conference participant is saying.

In some implementations, the conversational dynamics data may be appliedas one or more variables of a spatial optimization cost function. Thecost function may be a function of a vector describing a virtualconference participant position for each of a plurality of conferenceparticipants in a virtual acoustic space.

FIG. 8 is a flow diagram that outlines one example of a method accordingto some implementations of this disclosure. In some examples, the method800 may be performed by an apparatus, such as the apparatus of FIG. 3A.The blocks of method 800, like other methods described herein, are notnecessarily performed in the order indicated. Moreover, such methods mayinclude more or fewer blocks than shown and/or described.

In this implementation, block 805 involves receiving audio datacorresponding to a recording of a conference involving a plurality ofconference participants. According to some examples, the audio data maycorrespond to a recording of a complete or a substantially completeconference. In some implementations, in block 805 a control system, suchas the control system 330 of FIG. 3A, may receive the audio data via theinterface system 325.

In some implementations, the conference may be a teleconference, whereasin other implementations the conference may be an in-person conference.In this example, the audio data may include audio data from multipleendpoints, recorded separately. Alternatively, or additionally, theaudio data may include audio data from a single endpoint correspondingto multiple conference participants and including spatial informationfor each conference participant of the multiple conference participants.For example, the single endpoint may be a spatial speakerphone endpoint.

In some implementations, the audio data received in block 805 mayinclude output of a voice activity detection process. In somealternative implementations, method 800 may include a voice activitydetection process. For example, method 800 may involve identifyingspeech corresponding to individual conference participants.

In this example, block 810 involves analyzing the audio data todetermine conversational dynamics data. In this instance, theconversational dynamics data includes one or more of the following: dataindicating the frequency and duration of conference participant speech;data indicating instances of conference participant doubletalk duringwhich at least two conference participants are speaking simultaneously;and data indicating instances of conference participant conversations.

In this implementation, block 815 involves applying the conversationaldynamics data as one or more variables of a spatial optimization costfunction. Here, the spatial optimization cost function is a function ofa vector describing a virtual conference participant position for eachof the conference participants in a virtual acoustic space. Positionswithin the virtual acoustic space may be defined relative to theposition of a virtual listener's head. Some examples of suitable costfunctions are described below. During playback, the position of thevirtual listener's head may correspond with that of an actual listener'shead, particularly if the actual listener is wearing headphones. In thefollowing discussion, the terms “virtual listener's head” and“listener's head” may sometimes be used interchangeably. Likewise, theterms “virtual listener” and “listener” may sometimes be usedinterchangeably.

In this example, block 820 involves applying an optimization techniqueto the spatial optimization cost function to determine a solution. Inthis implementation, the solution is a locally optimal solution. Block820 may, for example, involve applying a gradient descent technique, aconjugate gradient technique, Newton's method, theBroyden-Fletcher-Goldfarb-Shanno algorithm; a genetic algorithm, analgorithm for simulated annealing, an ant colony optimization methodand/or a Monte Carlo method. In this implementation, block 825 involvesassigning the virtual conference participant positions in the virtualacoustic space based, at least in part, on the locally optimal solution.

For example, a variable of the cost function may be based, at least inpart, on conversational dynamics data indicating the frequency andduration of conference participant speech. As noted above, whenlistening to a long speech from one conversational participant (e.g.,during a business presentation) many listeners have indicated that theyprefer that conversational participant to be positioned in front ofthem, just as if they were present in a lecture or seminar. Accordingly,in some implementations, the spatial optimization cost function mayinclude a weighting factor, a penalty function, a cost or another suchterm (any and all of which may be referred to herein as a “penalty”)that tends to place conversational participants who speak frequently infront of the listener. For example, the spatial optimization costfunction may apply a penalty for placing conference participants whospeak frequently at virtual conference participant positions that arebeside, behind, above, or below the virtual listener's head.

Alternatively, or additionally, a variable of the cost function may bebased, at least in part, on conversational dynamics data indicatingconference participants who are involved in conference participantdoubletalk. It has been previously noted that rendering conferenceparticipants engaged in doubletalk to substantially different virtualconference participant positions can provide the listener an advantage,as compared with rendering conference participants engaged in doubletalkto the same virtual positions.

In order to quantify such differentiated positioning, someimplementations of the spatial optimization cost function may involveapplying a penalty for placing conference participants who are involvedin conference participant doubletalk at virtual conference participantpositions that are on, or close to lying on, a so-called “cone ofconfusion” defined relative to the virtual listener's head.

FIG. 9 shows an example of a virtual listener's head and a cone ofconfusion in a virtual acoustic space. In this example, a coordinatesystem 905 is defined relative to the position of a virtual listener'shead 910 within the virtual acoustic space 900. In this example, the yaxis of the coordinate system 905 coincides with the inter-aural axisthat passes between the ears 915 of the virtual listener's head 910.Here, the z axis is a vertical axis that passes through the center ofthe virtual listener's head 910 and the x axis is positive in thedirection that the virtual listener's head 910 is facing. In thisexample, the origin is midway between the ears 915.

FIG. 9 also shows an example of a cone of confusion 920, which isdefined relative to the inter-aural axis and the sound source 925 inthis example. Here, the sound source 925 is positioned at a radius Rfrom the inter-aural axis and is shown emitting sound waves 930. In thisexample, the radius R is parallel to the x and z axes and defines thecircular conical slice 935. Accordingly, all points along the circularconical slice 935 are equidistant from each of the ears 915 of thevirtual listener's head 910. Therefore, the sound from a sound sourcelocated anywhere on the circular conical slice 935, or any othercircular conical slice through the cone of confusion 920, will produceidentical inter-aural time differences. Such sounds also will producevery similar, though not necessarily identical, inter-aural leveldifferences.

Because of the identical inter-aural time differences, it can be verychallenging for a listener to distinguish the locations of sound sourcesthat are on, or close to, a cone of confusion. A sound source positionin the virtual acoustic space corresponds with a position to which thespeech of a conference participant will be rendered. Accordingly,because a source position in the virtual acoustic space corresponds witha virtual conference participant position, the terms “source” and“virtual conference participant position” may be used interchangeablyherein. If the voices of two different conference participants arerendered to virtual conference participant positions that are on, orclose to, a cone of confusion, the virtual conference participantpositions may seem to be the same, or substantially the same.

In order to sufficiently differentiate the virtual conferenceparticipant positions of at least some conference participants (such asthose who are engaged in doubletalk), it may be advantageous to define apredetermined angular distance from a cone of confusion, such as theangle α from the cone of confusion 920 that is shown in FIG. 9. Theangle α may define a conical annulus, inside and/or outside the cone ofconfusion 920, that has the same axis (here, the y axis) as the cone ofconfusion 920. Accordingly, some implementations of the spatialoptimization cost function may involve applying a penalty for placingconference participants who are involved in conference participantdoubletalk at virtual conference participant positions that are on, orwithin a predetermined angular distance from, a cone of confusiondefined relative to the virtual listener's head. In someimplementations, the penalty may be inversely proportional to theangular distance between the cones of confusion on which sources A and Blie. In other words, in some such implementations, the closer the twosources are to lying on a common cone of confusion, the larger thepenalty. In order to avoid abrupt changes and/or discontinuities, thepenalty may vary smoothly.

Alternatively, or additionally, a variable of the cost function may bebased, at least in part, on conversational dynamics data indicatinginstances of conference participant conversations. As noted above,rendering conference participants engaged in a conversation tosubstantially different virtual conference participant positions canimprove a listener's ability to distinguish which conference participantis talking at any given time and can improve the listener's ability tounderstand what each conference participant is saying. Accordingly, someimplementations of the spatial optimization cost function may involveapplying a penalty for placing conference participants who are involvedin a conference participant conversation with one another at virtualconference participant positions that are on, or within a predeterminedangular distance from, a cone of confusion defined relative to thevirtual listener's head. For example, the penalty may increase smoothlythe closer that the virtual conference participant positions are to acommon cone of confusion.

For conference participants who only make (or who principally make)short interjections during a conference, it may be acceptable, or evendesirable, to render the corresponding virtual conference participantpositions behind or beside the listener. A placement beside or behindthe listener evokes the metaphor of a question or comment from a fellowaudience member.

Therefore, in some implementations the spatial optimization costfunction may include one or more terms that tend to avoid rendering thevirtual conference participant positions corresponding to conferenceparticipants who only make (or who principally make) short interjectionsduring a conference to positions in front of the listener. According tosome such implementations, the spatial optimization cost function mayapply a penalty for placing conference participants who speakinfrequently at virtual conference participant positions that are notbeside, behind, above or below the virtual listener's head.

When conversing in a group setting, a listener may tend to move closerto a speaker to whom he or she wants to listen, instead of remaining ata distance. There may be social as well as acoustic reasons for suchbehaviour. Some implementations disclosed herein may emulate suchbehaviour by rendering the virtual conference participant positions ofconference participants who talk more frequently relatively closer tothe virtual listener than those who talk less frequently. For example,in some such implementations the spatial optimization cost function mayapply a penalty for placing conference participants who speak frequentlyat virtual conference participant positions that are farther from thevirtual listener's head than the virtual conference participantpositions of conference participants who speak less frequently.

According to some implementations, the cost function may be expressed asfollows:

F(α)=F _(conv)(α)+F _(dt)(α)+F _(front)(α)+F _(dist)(α)+F_(int)(α)  (Equation 1)

In Equation 1, F_(conv) represents the perceptual cost of violating theguideline that conversational participants who are engaged in aconversation should not be rendered at virtual conference participantpositions that lie on or near a cone of confusion. In Equation 1, F_(dt)represents the perceptual cost of violating the guideline thatconversational participants who are engaged in doubletalk should not berendered at virtual conference participant positions that lie on or neara cone of confusion. In Equation 1, F_(front) represents the perceptualcost of violating the guideline that conversational participants whospeak frequently should be rendered at virtual conference participantpositions that are in front of the listener. In Equation 1, F_(dist)represents the perceptual cost of violating the guideline thatconversational participants who speak frequently should be rendered atvirtual conference participant positions that are relatively closer tothe listener than conversational participants who speak less frequently.In Equation 1, F_(int) represents the perceptual cost of violating theguideline that conversational participants who offer only shortinterjections and/or speak infrequently should not be rendered atvirtual conference participant positions that are in front of thelistener.

In alternative implementations the cost function may include more, fewerand/or different terms. Some alternative implementations may omit theF_(int) variable and/or one or more other terms of Equation 1.

In Equation 1, α represents a vector describing the D-dimensionalvirtual conference participant positions, in a virtual acoustic space,of each of N conference participants. For example, if a renderer hasthree degrees of freedom per position (such that D=3) and these are thepolar (Euler angle) coordinates of azimuth angle (θ_(i)), elevationangle (ϕ_(i)) and distance (d_(i)) for a given source i (where 1≤i≤N)then the vector α could be defined as follows:

$\begin{matrix}{a = \begin{bmatrix}\theta_{1} \\\varphi_{1} \\d_{1} \\\vdots \\\theta_{N} \\\varphi_{N} \\d_{N}\end{bmatrix}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

However, in many cases one may obtain a simpler and more numericallystable solution by instead working in Cartesian coordinates. Forexample, we can define an (x,y,z) coordinate system such as that shownin FIG. 9. In one such example, we could define x_(i) to be the distanceof source i (such as the sound source 925 of FIG. 9) from the center ofthe virtual listener's head along an axis extending outwards from thelistener's nose in front of the listener. We can define y_(i) to be thedistance of source i from the center of the listener's head along anaxis extending to the left of the listener, perpendicular to the firstaxis. Lastly we can define z_(i) to be the distance of source i from thecenter of the listener's head along an axis extending upwards,perpendicular to both the other axes. The units of distance used may bearbitrary. However, in the following description we will assume thatdistances are normalized to suit the rendering system so that at avirtual distance of one unit from the listener, the listener's abilityto localise the source will be maximized.

If we use the Cartesian coordinate system just described, then vector αcould be defined as follows:

$\begin{matrix}{a = \begin{bmatrix}x_{1} \\y_{1} \\z_{1} \\\vdots \\x_{N} \\y_{N} \\z_{N}\end{bmatrix}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

The foregoing paragraphs provide an example of a perceptual costfunction F(α), which describes the fitness (suitability) of a particularvector α of virtual conference participant positions according tovarious types of conversational dynamics data. We can now find a vectorof source locations a_(opt), which results in the minimum perceptualcost (in other words, the maximum fitness). Given the foregoing novelcost function, some implementations may involve applying known numericaloptimisation techniques to find a solution, such as a gradient descenttechnique, a conjugate gradient technique, Newton's method, theBroyden-Fletcher-Goldfarb-Shanno algorithm; a genetic algorithm, analgorithm for simulated annealing, an ant colony optimization methodand/or a Monte Carlo method. In some implementations, the solution maybe a locally optimal solution, for which the above-mentioned exampletechniques are known to be well-suited.

In some embodiments, the input to a spatial optimization cost functionmay be a matrix V of VAD (voice activity detector) output. For example,the matrix may have one row for each discrete temporal analysis framefor the conference and may have N columns, one for each conferenceparticipant. In one such example, our analysis frame size might be 20ms, which means that V contains the VAD's estimate of the probabilitythat each 20 ms analysis frame of each source contains speech. In otherimplementations, the analysis frame may correspond with a different timeinterval. For the sake of simplicity, let us further assume that in theexample described below, each VAD output may be either 0 or 1. That is,the VAD output indicates that each source either does, or does not,contain speech within each analysis frame.

To further simplify the discussion, we may assume that the optimizedplacement of virtual conference participant positions takes place afterthe conference recording is complete, so that the process may haverandom access to all of the analysis frames for the conference. However,in alternative examples, a solution may be generated for any portion ofa conference, such as an incomplete recording of the conference, takinginto account the VAD information generated for that portion of theconference.

In this example, the process may involve passing the matrix V throughaggregation processes in order to generate aggregate features of theconference. According to some such implementations, the aggregatefeatures may correspond to instances of doubletalk and turn-takingduring the conference. According to one such example, the aggregatefeatures correspond to a doubletalk matrix C_(dt) and a turn-takingmatrix C_(turn).

For example, C_(dt) may be a symmetric N×N matrix describing in row i, jthe number of analysis frames during the conference that conferenceparticipants i and j simultaneously contained speech. The diagonalelements of C_(dt) therefore describe the number of frames of speechfrom each conference participant and the other elements of the matrixdescribe the number of frames a particular pair of conferenceparticipants engaged in doubletalk during the conference.

In some implementations, an algorithm to compute C_(dt) may proceed asfollows. First, C_(dt) may be initialized so that all elements are zero.Then, each row v of V (in other words, each analysis frame) may beconsidered in turn. For each frame, one may be added to each elementc_(ij) of C_(dt) where columns i and j of v are both non-zero.Alternatively, C_(dt) may be computed by matrix multiplication, e.g., asfollows:

C _(dt) =V ^(T) V  (Equation 4)

In Equation 4, V^(T) represents the conventional matrix transposeoperation applied to matrix V.

A normalized doubletalk matrix N_(dt) may then be created by dividingC_(dt) by the total amount of talk in the conference (in other words,the trace of the matrix C_(dt)), e.g., as follows:

$\begin{matrix}{N_{dt} = \frac{C_{dt}}{{tr}\left( C_{dt} \right)}} & \left( {{Equation}\mspace{14mu} 5} \right)\end{matrix}$

In Equation 5, tr(C_(dt)) represents the trace of the matrix C_(dt).

In order to compute C_(turn), after initializing to zero, someimplementations involve locating the onset of each talkspurt. Forexample, some implementations may involve considering each conferenceparticipant i in V, and finding each row r in V, where there is a zeroin column i and a one in row r+1. Then, for each talkspurt, some suchexamples involve determining which conference participant j mostrecently spoke prior to that talkspurt. This will be an example of“turn-taking” involving conference participants i and j, which also maybe referred to herein as an example of a “turn.”

Such examples may involve looking backwards in time (in other words,looking in rows r and above) in order to identify which conferenceparticipant j most recently spoke prior to that talkspurt. In some suchexamples, a “1” may be added to row i, column j of C_(turn) for eachsuch instance of turn-taking found. C_(turn) may, in general, benon-symmetrical because it retains information pertaining to temporalorder.

Given the foregoing information, a normalized turn-taking matrixN_(turn) may be created, e.g., by dividing C_(turn) by the total numberof turns in the conference (in other words, by the sum of all theelements in the matrix), for example as follows:

$\begin{matrix}{N_{turn} = \frac{C_{turn}}{\sum_{i}{\sum_{j}C_{{turn},{ij}}}}} & \left( {{Equation}\mspace{14mu} 6} \right)\end{matrix}$

In Equation 6, Σ_(i)Σ_(j) C_(turn,ij) represents the sum of all theelements in the C_(turn) matrix. In alternative implementations, thematrices C_(dt) and C_(turn), as well as the normalization factorstr(C_(dt)) and Σ_(i)Σ_(j) C_(turn,ij), may be computed by analyzing theVAD output one analysis frame at a time. In other words, it is notnecessary to have the entire matrix V available at one time. In additionto C_(dt), C_(turn), tr(C_(dt)) and Σ_(i)Σ_(j) C_(turn,ij), some suchmethods require only that the identity of the most recent talker be keptas state, as the process iteratively analyzes the VAD output one frameat a time.

In some implementations, the aggregate features N_(dt) and N_(turn) mayform the input to the spatial optimization cost function, along with aninitial condition for position vector α. Almost any set of initialvirtual conference participant positions is suitable. However, it ispreferable that any two sources are not initially co-located, e.g., inorder to ensure that the gradient of the cost function is well-defined.Some implementations involve making all of the initial virtualconference participant positions behind the listener. In some suchimplementations, the cost function may not include the F_(int) term or acorresponding term that tends to move the virtual conference participantpositions of interjectors/infrequent talkers to positions behind thelistener. In other words, two general options are as follows: (a) makeall of the initial virtual conference participant positions behind thelistener and omit the F_(int) term or a corresponding term; or (b)include the F_(int) term or a corresponding term and make the initialvirtual conference participant positions at any convenient locations.F_(front) may be small for interjectors because they talk infrequently.Therefore, implementations that involve option (a) may not have a strongtendency to move interjectors towards the front of the listener.

FIG. 10 shows an example of initial virtual conference participantpositions in a virtual acoustic space. The coordinate system of thevirtual acoustic space shown in FIG. 10, like that shown in FIG. 9, isbased on the position of the virtual listener's head 910. In thisexample, 11 initial virtual conference participant positions are shown,each of which has been determined according to the following:

$\begin{matrix}{x_{i} = {- 0.5}} & \left( {{Equation}\mspace{14mu} 7} \right) \\{y_{i} = {{- 1} + \frac{2i}{N - 1}}} & \left( {{Equation}\mspace{14mu} 8} \right) \\{z_{i} = {{{- 1} + \frac{2i}{N - 1}}}} & \left( {{Equation}\mspace{14mu} 9} \right)\end{matrix}$

In Equations 7-9, x_(i), y_(i) and z_(i) represent the initial (x,y,z)coordinates of conversational participant i and N represents the totalnumber of conversational participants. In FIG. 10, the numbered dotscorrespond to the virtual conference participant positions. The dot sizeindicates the relative amount of speech for the corresponding conferenceparticipant, with a larger dot indicating relatively more speech. Thevertical lines attached to the dots indicate the distance above thehorizontal plane, corresponding to the z coordinate for each virtualconference participant position. A unit sphere 1005, the surface ofwhich is at a distance of one unit from the origin, is shown forreference.

In one example, a gradient descent optimization may be performed byapplying the following formula (at iteration k) until a convergencecriterion is reached:

α_(k+1)=α_(k)−β_(k) ∇F(α_(k))  (Equation 10)

In Equation 10, β_(k) represents an appropriate step size, which isdiscussed in further detail below. In one example, one may count thenumber of successive optimisation steps n in which the followingcondition holds:

|F(α_(k+1))−F(α_(k))|<T  (Equation 11)

In Equation 11, T represents a constant, which may be set to anappropriately small value. A suitable example value for the constant Tfor some implementations is 10⁻⁵. In alternative implementations, T maybe set to another value. However, in such alternative implementations, Tmay be orders of magnitude smaller than an average cost F(a), e.g.,averaged over a large number of conference conditions. In some examples,a convergence criterion may be n≥10, indicating that the change in costover the last 10 consecutive optimisation steps has been very small andwe are now very close to a local minimum (or at least in a very “flat”region of the cost function where any further change is unlikely to beperceived by the listener).

For the sake of clarity in the following discussion, note that we canwrite the gradient expression from equation 10 in expanded form asfollows:

$\begin{matrix}{{\nabla{F(a)}} = \begin{bmatrix}\frac{\partial{F(a)}}{\partial x_{1}} \\\frac{\partial{F(a)}}{\partial y_{1}} \\\frac{\partial{F(a)}}{\partial z_{1}} \\\vdots \\\frac{\partial{F(a)}}{\partial x_{N}} \\\frac{\partial{F(a)}}{\partial y_{N}} \\\frac{\partial{F(a)}}{\partial z_{N}}\end{bmatrix}} & \left( {{Equation}\mspace{14mu} 12} \right)\end{matrix}$

FIG. 11 shows examples of final virtual conference participant positionsin a virtual acoustic space. FIG. 11 shows an example of applying theforegoing process for 11 conversational participants, given the initialvirtual conference participant positions shown in FIG. 10. In thisexample, all of the final virtual conference participant positions areon or near the unit sphere 1005. In FIG. 11, all of the largest dots,which correspond with conversational participants who speak the mostfrequently, have been moved in front of the virtual listener's head 910.The small dots corresponding to conversational participants 1 and 3 arethe smallest, indicating that these conversational participants speakthe least frequently and have therefore remained behind the virtuallistener's head 910. In this example, the dots corresponding toconversational participants 5 and 8 are small, but slightly larger thanthose of conversational participants 1 and 3, indicating that theseconversational participants somewhat more frequently than conversationalparticipants 1 and 3, but not as much as the other conversationalparticipants. Therefore, the dots corresponding to conversationalparticipants 5 and 8 have drifted forward from their initial positionsbehind the virtual listener's head 910 somewhat, but not very strongly.The virtual conference participant positions corresponding toconversational participants 5 and 8 remain above the virtual listener'shead 910 due to the effect of F_(dist), which tends, in this embodiment,to keep all of the virtual conference participant positions at a radiusof one unit from the origin.

Following is a more detailed description of the terms of Equation 1,according to some implementations. In some examples, the term ofEquation 1 that corresponds with conversational dynamics data involvingconference participant conversations may be determined as follows:

F _(conv)(α)=Σ_(i=1) ^(N)Σ_(j=1) ^(N) F _(conv,ij)(α)  (Equation 13)

In Equation 13, F_(conv,ij)(α) represents the component of costcontributed by the pair of sources i and j being near a cone ofconfusion. Since the sources are on a cone of confusion if their ycoordinates are equal (assuming they lie on a unit sphere), in someexamples, F_(conv,ij)(α) may be determined as follows:

$\begin{matrix}{{F_{{conv},{ij}}(a)} = \left\{ \begin{matrix}{0,} & {{{if}\mspace{14mu} i} = j} \\\frac{K_{conv}N_{{turn},{ij}}}{\left( {y_{i} - y_{j}} \right)^{2} + ɛ} & {otherwise}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 14} \right)\end{matrix}$

In Equation 14, K_(conv) and ε represent constants. In some examples,both constants may be set to relatively small values, such as 0.001. Inthis example, ε prevents the cost from reaching an infinite value whenthe sources lie exactly on a cone of confusion. K_(conv) may be tunedwith regard to the other parameters in order to achieve good separationwhile also allowing several sources to be in front. If K_(conv) is settoo high, F_(conv) will tend to dominate all the other cost functionelements and just spread the sources all around the sphere. Accordingly,while alternative values of K_(conv) and ε may be used in variousimplementations, these and other parameters are inter-related and can bejointly tuned to produce desired results.

An underlying assumption of Equation 14 is that the sources lie on aunit sphere, because F_(dist)(α) (one example of which is morespecifically defined below) will, in some implementations, reliably keepsources near the unit sphere. If F_(dist)(α) is alternatively definedsuch that it does not reliably keep sources near the unit sphere, thenit may be necessary to normalise the y coordinates prior to calculatingF_(conv,ij)(α), e.g., as follows:

$\begin{matrix}{{\hat{y}}_{i} = \frac{y_{i}}{\sqrt{x_{i}^{2} + y_{i}^{2} + z_{i}^{2}}}} & \left( {{Equation}\mspace{14mu} 15} \right) \\{{F_{{conv},{ij}}(a)} = \left\{ \begin{matrix}{0,} & {{{if}\mspace{14mu} i} = j} \\{\frac{K_{conv}N_{{turn},{ij}}}{\left( {{\hat{y}}_{i} - {\hat{y}}_{j}} \right)^{2} + ɛ},} & {otherwise}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 16} \right)\end{matrix}$

Some alternative examples may involve directly calculating a costproportional to the reciprocal of the inter-aural time differences.

In some implementations, F_(dt)(α) may be calculated as follows:

F _(dt)(α)=Σ_(i=1) ^(N)Σ_(j=1) ^(N) F _(dt,ij)(α)  (Equation 17)

In some examples, the term F_(dt,ij)(α) of Equation 17 may be determinedas follows:

$\begin{matrix}{{F_{{dt},{ij}}(a)} = \left\{ \begin{matrix}{0,} & {{{if}\mspace{14mu} i} = j} \\{\frac{K_{dt}N_{{dt},{ij}}}{\left( {y_{i} - y_{j}} \right)^{2} + ɛ},} & {otherwise}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 18} \right) \\{\frac{\partial F_{{dt},{ij}}}{\partial y_{i}} = \frac{{- 2}K_{dt}N_{{dt},{ij}}}{\left\lbrack {\left( {y_{i} - y_{j}} \right)^{2} + ɛ} \right\rbrack^{2}}} & \left( {{Equation}\mspace{14mu} 19} \right) \\{\frac{\partial F_{{dt},{ij}}}{\partial y_{j}} = \frac{2K_{dt}N_{{dt},{ij}}}{\left\lbrack {\left( {y_{i} - y_{j}} \right)^{2} + ɛ} \right\rbrack^{2}}} & \left( {{Equation}\mspace{14mu} 20} \right)\end{matrix}$

In Equations 18-20, K_(dt) and ε represent constants. In some examples,K_(dt) may be 0.002 and ε may be 0.001. Although various other values ofK_(dt) and ε may be used in alternative implementations, these and otherparameters are inter-related and can be jointly tuned to produce desiredresults.

In some implementations, the variable F_(front)(α) of Equation (1)imposes a penalty for not being in front of the listener which isproportional to the square of how much a conversational participant hasparticipated in the conference. As a result, the virtual conferenceparticipant positions for conversational participants who talkrelatively more end up relatively closer to a front, center position,relative to a virtual listener in the virtual acoustic space. In somesuch examples, F_(front)(α) may be determined as follows:

F _(front)(α)=Σ_(i=1) ^(N) F _(front,i)(α)  (Equation 21)

F _(front,i)(α)=K _(front) N _(dt,ii) ²[(x _(i)−1)² +y _(i) ² +z _(i)²]  (Equation 22)

In Equation 22, K_(front) represents a constant, which in some examplesmay be 5. Although various other values of K_(front) may be used inalternative implementations, this parameter may be inter-related withothers. For example, K_(front) should be large enough to pull thevirtual conference participant positions for conversational participantswho talk the most to the front, but not so large that F_(front)consistently overpowers the contributions of F_(conv) and F_(dt). Insome examples, the contribution to the gradient due to F_(front)(a) maybe determined as follows:

$\begin{matrix}{\frac{\partial F_{{front},i}}{\partial x_{i}} = {2K_{front}{N_{{dt},{ii}}^{2}\left( {x_{i} - 1} \right)}}} & \left( {{Equation}\mspace{14mu} 23} \right) \\{\frac{\partial F_{{front},i}}{\partial y_{i}} = {2K_{front}N_{{dt},{ii}}^{2}y_{i}}} & \left( {{Equation}\mspace{14mu} 24} \right) \\{\frac{\partial F_{{front},i}}{\partial z_{i}} = {2K_{front}N_{{dt},{ii}}^{2}z_{i}}} & \left( {{Equation}\mspace{14mu} 25} \right)\end{matrix}$

In some implementations, the F_(dist)(α) component of Equation 1 mayimpose a penalty for not placing virtual conference participantpositions on the unit sphere. In some such examples, the penalty may behigher for conference participants who talk more. In some instances,F_(dist)(α) may be determined as follows:

F _(dist)(α)=Σ_(i=1) ^(N) F _(dist,i)(α)  (Equation 26)

F _(dist,i)(α)=K _(dist) N _(dt,ii) [x _(i) ² +y _(i) ² +z _(i)²−1]²  (Equation 27)

In Equation 27, K_(dist) represents a constant, which in some examplesmay be 1. Although various other values of K_(dist) may be used inalternative implementations, this parameter may be inter-related withothers. For example, if K_(dist) is made too small, the effect ofF_(dist) may be too weak and sources will tend to drift from the unitsphere. In some examples, the contribution to the gradient due toF_(dist)(α) may be determined as follows:

$\begin{matrix}{\frac{\partial F_{{dist},i}}{\partial x_{i}} = {4K_{dist}N_{{dt},{ii}}{x_{i}\left\lbrack {x_{i}^{2} + y_{i}^{2} + z_{i}^{2} - 1} \right\rbrack}}} & \left( {{Equation}\mspace{14mu} 28} \right) \\{\frac{\partial F_{{dist},i}}{\partial y_{i}} = {4K_{dist}N_{{dt},{ii}}{y_{i}\left\lbrack {x_{i}^{2} + y_{i}^{2} + z_{i}^{2} - 1} \right\rbrack}}} & \left( {{Equation}\mspace{14mu} 29} \right) \\{\frac{\partial F_{{dist},i}}{\partial z_{i}} = {4K_{dist}N_{{dt},{ii}}{z_{i}\left\lbrack {x_{i}^{2} + y_{i}^{2} + z_{i}^{2} - 1} \right\rbrack}}} & \left( {{Equation}\mspace{14mu} 30} \right)\end{matrix}$

In some embodiments, the term F_(int)(α) of Equation 1 may be set tozero. This may acceptable, for example, in implementations for which theinitial conditions place sources behind the virtual listener's head.Because various implementations of F_(front)(α) place only a weakpenalty for sources that talk very little being behind the listener,they will tell to stay behind the virtual listener's head unless theconvergence criterion is extremely tight. In some alternativeembodiments a small penalty may be associated with any source that isnot behind the virtual listener's head. In many implementations, thissmall penalty would tend to be dominated by F_(front)(α) except in thecase of conversational participants who talk very little.

Some more detailed examples of convergence criteria and processes willnow be described. Referring again to Equation 10, some implementationsinvolve adapting the step size β_(k) as optimization proceeds by the useof a so-called line search. In some such implementations, the value ofβ⁻¹ may be initialized to 0.1. According to some such examples, at eachstep, β_(k) may be adapted according to the following process:

1. Assume {circumflex over (β)}_(k)=β_(k-1).

2. Compute F₁=F(α_(k)−{circumflex over (β)}_(k)∇F(α_(k))), the new costat step size {circumflex over (β)}_(k).

3. If F₁>F(α_(k)), then stepping by {circumflex over (β)}_(k) willovershoot the minimum, so halve {circumflex over (β)}_(k) and return tostep 2.

4. Compute F₂=F(α_(k)−2{circumflex over (β)}_(k)∇F(α_(k))), the new costat step size 2{circumflex over (β)}_(k).

5. If F₁>F₂, then stepping by 2{circumflex over (β)}_(k) stillundershoot the minimum, so double {circumflex over (β)}_(k) and returnto step 2.

6. A step size somewhere between {circumflex over (β)}_(k) and2{circumflex over (β)}_(k) should result in a value near the minimum.Some examples operate under the assumption that the shape of the costfunction can be approximated by a quadratic in {circumflex over (β)}_(k)through the points (0, F(α_(k))), ({circumflex over (β)}_(k), F₁),(2{circumflex over (β)}_(k), F₂) and find the minimum as follows:

$\begin{matrix}{\beta_{k} = {{\hat{\beta}}_{k} + \frac{F_{2} - {F\left( a_{k} \right)}}{{2F_{1}} - {3{F\left( a_{k} \right)}} - F_{2}}}} & \left( {{Equation}\mspace{14mu} 31} \right)\end{matrix}$

7. Then, clamp {circumflex over (β)}_(k) to ensure it lies in[{circumflex over (β)}_(k), 2{circumflex over (β)}_(k)].

In some embodiments, the spatial optimization cost function may takeinto account the perceptual distinctiveness of the conversationalparticipants. It is well documented that simultaneous talkers are betterunderstood when their voices are perceived to be very distinct. This hasbeen observed when the traits that give rise to the distinctiveness ofvoices are described as categorical (e.g., if talkers are recognized asbeing male or female, or if a voice is perceived as “clean” or “noisy”)or continuous (e.g., voice pitch, vocal tract length, etc.)

Accordingly, some implementations may involve determining whichconference participants, if any, have perceptually similar voices. Insome such implementations, a spatial optimization cost function mayapply a penalty for placing conference participants with perceptuallysimilar voices at virtual conference participant positions that are on,or within a predetermined angular distance from, a cone of confusiondefined relative to a virtual listener's head. Some such implementationsmay involve adding another variable to Equation 1.

However, alternative implementations may involve modifying one of thevariables of Equation 1. For example, while some implementations ofF_(conv)(α) and F_(dt)(α) are designed to penalise locating conferenceparticipants who converse and doubletalk respectively in confusablespatial placements, some alternative implementations involve modifyingF_(conv)(α) and/or F_(dt)(α) to further penalize such placements if thevoices of the conference participants in question are perceptuallysimilar.

Some such examples may involve a third N×N aggregate matrix N_(dsim)which quantifies the dissimilarity of each pair of conferenceparticipants involved in a conference. To calculate N_(dsim), someimplementations first determine a “characteristic feature vector” sconsisting of B characteristic features from each conference participantin a conference recording, where each characteristic feature s[k]_(i) isa perceptually relevant measure of talker i. One example in which B=2 isas follows:

$\begin{matrix}{s_{i} = \begin{bmatrix}{s\lbrack 1\rbrack}_{i} \\{s\lbrack 2\rbrack}_{i}\end{bmatrix}} & \left( {{Equation}\mspace{14mu} 32} \right)\end{matrix}$

In Equation 32, s[1]_(i) represents the median voice pitch and s[2]_(i)represents the estimated vocal tract length of conference participant i.The characteristic features may be estimated by aggregating informationfrom many, possibly all, speech utterances the conference participantmade during the conference. In other implementations othercharacteristic features, such as accents and speaking rate, may be usedto quantify the dissimilarity of a pair of conference participants.Still other implementations may involve quantifying the similarity,rather than the dissimilarity, of a pair of conference participants.

In some implementations, the characteristic feature vector may beproduced by a bank of B time-domain filters, each of which may befollowed by an envelope detector with appropriate time constant. Thecharacteristic feature vector may be produced by applying a discreteFourier transform (DFT), which may be preceded by appropriate windowingand followed by an appropriate banding process. The banding process maygroup DFT bins into bands of approximately equal perceptual size. Insome examples, Mel frequency cepstral coefficients may be calculatedafter the DFT and banding process. If the conference is stored in anencoded format that makes use of frequency domain coding (e.g.,according to a modified discrete cosine transform (MDCT) process), someimplementations may use the coding domain coefficients followed byappropriate banding.

In some implementations, the characteristic feature vector may beproduced by linear prediction coefficients, such as those used in linearpredictive coding (LPC) schemes. Some examples may involve perceptuallinear prediction (PLP) methods, such as those used for speechrecognition.

According to some implementations, after calculation of thecharacteristic feature vector a suitable distance metric may be appliedbetween each pair of characteristic feature vectors s_(i), s_(j) tocalculate each element in N_(dsim). An example of such a distance metricis the mean square difference, which may be calculated as follows:

$\begin{matrix}{N_{{dsim},{ij}} = {\frac{1}{B}{\sum\limits_{k = 1}^{B}\left( {{s_{i}(k)} - {s_{j}(k)}} \right)^{2}}}} & \left( {{Equation}\mspace{14mu} 33} \right)\end{matrix}$

In Equation 33, k represents an index of one of the B characteristicfeatures in s (in this example, s is a B-dimensional or B-featurevector). According to Equation 33, each of the features is considered,the difference between each two features is determined, that differenceis squared and summed over all dimensions. For example, for thetwo-dimensional example given in Equation 32, B is 2 and the sum overthe variable k takes on values k=1 and k=2, corresponding to the literalnumbers 1 and 2 seen in Equation 32. Some implementations may involvecomputing a characteristic feature vector s for a particular conferenceparticipant based on information spanning multiple conferences. Somesuch implementations may involve determining a long-term average ofbased on audio data for multiple conferences.

In some implementations, there may be a priori knowledge of the genderof conference participants. For example, conference participants may berequired or encouraged to specify whether they are male or female aspart of a registration or enrolment process. When such knowledge isavailable to the playback system, an alternative example method forcalculating N_(dsim,ij) may be as follows:

$\begin{matrix}{N_{{dsim},{ij}} = \left\{ \begin{matrix}{K_{homo},} & {{if}\mspace{14mu} {talkers}\mspace{14mu} i\mspace{14mu} {and}\mspace{14mu} j\mspace{14mu} {are}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {same}\mspace{14mu} {sex}} \\{K_{hetero},} & {{if}\mspace{14mu} {talkers}\mspace{14mu} i\mspace{14mu} {and}\mspace{14mu} j\mspace{14mu} {are}\mspace{14mu} {of}\mspace{14mu} {different}\mspace{14mu} {sexes}}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 34} \right)\end{matrix}$

In Equation 34, K_(homo) and K_(hetero) represent constants. In oneexample, K_(homo) may equal 1.0 and K_(hetero) may be, for example, inthe range [0.1, 0.9]*K_(homo), or equal to 0.5.

Based on any of the foregoing examples, one can redefine F_(conv,ij)(a)and F_(dt,ij)(a) to include the spectral similarity aggregateN_(dsim,ij), e.g., as follows:

$\begin{matrix}{{F_{{conv},{ij}}(a)} = \left\{ \begin{matrix}{0,} & {{{if}\mspace{14mu} i} = j} \\{\frac{K_{conv}N_{{turn},{ij}}N_{{dsim},{ij}}}{\left( {y_{i} - y_{j}} \right)^{2} + ɛ},} & {otherwise}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 35} \right) \\{{F_{{dt},{ij}}(a)} = \left\{ \begin{matrix}{0,} & {{{if}\mspace{14mu} i} = j} \\{\frac{K_{dt}N_{{dt},{ij}}N_{{dsim},{ij}}}{\left( {y_{i} - y_{j}} \right)^{2} + ɛ},} & {otherwise}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 36} \right)\end{matrix}$

According to some embodiments, assigning a virtual conferenceparticipant position may involve selecting a virtual conferenceparticipant position from a set of predetermined virtual conferenceparticipant positions. In some such examples, each source may only beplaced in one of a fixed set of virtual conference participant positionsof size A. In such implementations, each cost function component may becalculated directly via table lookup rather than by calculation based onposition coordinates. For example, each cost function component may becalculated as follows:

F _(conv,ij)(α)=K _(conv,ij) N _(turn,ij) N _(dsim,ij)  (Equation 37)

In Equation 37, represents a fixed matrix (for example, a look-up table)that describes to what extent speech from position i will perceptuallymask speech from position j. K_(conv,ij) may be derived, for example,from large-scale subjective tests. In this example, the optimizationprocess involves assigning each source to one of the A virtualconference participant positions. Because the search space is no longercontinuous, in such examples discrete optimization techniques (such assimulated annealing and genetic algorithms) may be relatively moreapplicable than some other optimization techniques referred to herein.

Some implementations may involve a hybrid solution, in which somevirtual conference participant positions are assigned to predeterminedvirtual conference participant positions and other virtual conferenceparticipant positions are determined without reference to predeterminedvirtual conference participant positions. Such implementations may beused, for example, when the number of virtual conference participantpositions to be determined exceeds the number of predetermined virtualconference participant positions. In some such examples, if there are Apredetermined virtual conference participant positions but more than Avirtual conference participant positions to be determined, thepredetermined virtual conference participant positions may be used forthe A conference participants who talk the most and dynamic positionsmay be calculated for the remaining conference participants, e.g., byusing a spatial optimization cost function such as that of Equation 1.

Some implementations disclosed herein allow a listener to play backand/or scan through a conference recording quickly, while maintainingthe ability to attend to words, topics and talkers of interest. Somesuch implementations reduce playback time by taking advantage of spatialrendering techniques and of introducing (or changing) overlap betweeninstances of conference participant speech according to a set ofperceptually-motivated rules. Alternatively, or additionally, someimplementations may involve speeding up the played-back conferenceparticipant speech.

FIG. 12 is a flow diagram that outlines one example of a methodaccording to some implementations of this disclosure. In some examples,the method 1200 may be performed by an apparatus, such as the apparatusof FIG. 3A and/or one or more components of the playback system 609 ofFIG. 6. In some implementations, the method 1200 may be performed by atleast one device according to software stored on one or morenon-transitory media. The blocks of method 1200, like other methodsdescribed herein, are not necessarily performed in the order indicated.Moreover, such methods may include more or fewer blocks than shownand/or described.

In this implementation, block 1205 involves receiving audio datacorresponding to a recording of a conference involving a plurality ofconference participants. In some implementations, in block 1205 acontrol system, such as the control system 330 of FIG. 3A, may receivethe audio data via the interface system 325.

In some implementations, the conference may be a teleconference, whereasin other implementations the conference may be an in-person conference.In this example, the audio data may include audio data from multipleendpoints, recorded separately. Alternatively, or additionally, theaudio data may include audio data from a single endpoint correspondingto multiple conference participants and including spatial informationfor each conference participant of the multiple conference participants.For example, the single endpoint may include a microphone array, such asthat of a soundfield microphone or a spatial speakerphone. According tosome examples, the audio data may correspond to a recording of acomplete or a substantially complete conference.

In some implementations, the audio data may include output of a voiceactivity detection process. Accordingly, in some such implementationsthe audio data may include indications of speech and/or non-speechcomponents. However, if the audio data does not include output of avoice activity detection process, in some examples method 1200 mayinvolve identifying speech corresponding to individual conferenceparticipants. For implementations in which conference participant speechdata from a single endpoint corresponding to multiple conferenceparticipants is received in block 1205, method 1200 may involveidentifying speech corresponding to individual conference participantsaccording to the output of a “speaker diarization” process ofidentifying the conference participant who uttered each instance of thespeech.

In this example, block 1210 involves rendering the conferenceparticipant speech data for each of the conference participants to aseparate virtual conference participant position in a virtual acousticspace. In some implementations, block 1210 may involve virtualconference participant positions as described elsewhere herein.

Accordingly, in some such implementations, block 1210 may involveanalyzing the audio data to determine conversational dynamics data. Insome instances, the conversational dynamics data may include dataindicating the frequency and duration of conference participant speech,data indicating instances of conference participant doubletalk duringwhich at least two conference participants are speaking simultaneouslyand/or data indicating instances of conference participantconversations. Some implementations may involve analyzing the audio datato determine other types of conversational dynamics data and/or thesimilarity of conference participant speech.

In some such implementations, block 1210 may involve applying theconversational dynamics data as one or more variables of a spatialoptimization cost function. The spatial optimization cost function maybe a function of a vector describing a virtual conference participantposition for each of the conference participants in a virtual acousticspace. Positions within the virtual acoustic space may be definedrelative to the position of a virtual listener's head. Block 1210 mayinvolve applying an optimization technique to the spatial optimizationcost function to determine a locally optimal solution and assigning thevirtual conference participant positions in the virtual acoustic spacebased, at least in part, on the locally optimal solution.

However, in other implementations block 1210 may not involve a spatialoptimization cost function. For example, in some alternativeimplementations, block 1210 may involve rendering the conferenceparticipant speech data for each of the conference participants to aseparate one of multiple predetermined virtual conference participantpositions. Some alternative implementations of block 1210 may involvedetermining the virtual conference participant positions withoutreference to conversational dynamics data.

In various implementations, method 1200 may involve playing back theconference participant speech according to a set ofperceptually-motivated rules. In this example, block 1215 involvesplaying back the conference participant speech such that at least someof the conference participant speech that did not previously overlap intime is played back in an overlapped fashion, according to the set ofperceptually-motivated rules.

According to methods such as method 1200, a listener may benefit fromthe binaural advantage offered by playing back audio data for each ofmultiple conference participants from various unique locations in space.For example, the listener may be able to tolerate significant overlap ofspeech from conference participants, rendered to different locations,and yet maintain the ability to attend to (without loss of generality)words, topics, sounds or talkers of interest. In some implementations,once a section of interest has been identified, the listener may havethe option of switching to a non-overlapped playback mode to listen inmore detail to that section, e.g., via interaction with one or moreelements of a playback system such as the playback system 609 of FIG. 6.

The rules applied in method 1200, and in other methods provided herein,are referred to as “perceptually-motivated” because they are based onreal-world listening experiences. For example, in some implementationsthe set of perceptually-motivated rules may include a rule indicatingthat two sections of speech of a single conference participant shouldnot overlap in time. This rule is motivated by the observation that,while it is a natural part of human experience to hear multiple talkersspeaking concurrently (for example, at a cocktail party), it is not anatural experience to hear two copies of the same talker speakingconcurrently. In the real world humans may only utter a single stream ofspeech at a time and, generally, each human has a uniquely identifiablespeaking voice.

Some implementations may involve one or more variants of the foregoingrule. For example, in some implementations the set ofperceptually-motivated rules may include a rule indicating that twosections of speech should not overlap in time if the two sections ofspeech correspond to a single endpoint. In many instances, a singleendpoint will correspond with only a single conference participant. Insuch instances, this variant is another way of expressing the foregoingrule against two sections of speech of a single conference participantoverlapping in time. However, in some implementations this variant maybe applied even for single endpoints that correspond with multipleconference participants.

In some implementations, the set of perceptually-motivated rules mayseek to prevent the order of what is said, during discussions and/orinteractions between multiple conference participants, from becomingdisordered in an unnatural manner. For example, in the real world oneconference participant may answer a question before another conferenceparticipant has finished articulating the question. However, one wouldgenerally not expect to hear a complete answer to a question, followedby the question itself.

Consider two consecutive input talkspurts A and B, wherein talkspurt Aoccurs before talkspurt B. According to some implementations, the set ofperceptually-motivated rules may include a rule allowing the playback ofan output talkspurt corresponding to B to begin before the playback ofan output talkspurt corresponding to A is complete, but not before theplayback of the output talkspurt corresponding to A has started.

In some implementations, an upper bound (sometimes referred to herein asT) may be imposed on the amount of overlap that is introduced betweenany two consecutive input talkspurts (such as A and B), in order toprevent a significant degree of acausality of playback duringdiscussions and/or interactions between multiple conferenceparticipants. Therefore, in some examples the set ofperceptually-motivated rules may include a rule allowing the playback ofthe output talkspurt corresponding to B to begin no sooner than a time Tbefore the playback of the output talkspurt corresponding to A iscomplete.

In some instances, the recorded audio data may include input talkspurtsthat previously overlapped in time (during the original conference). Insome implementations, the set of perceptually-motivated rules mayinclude one or more rules indicating that output talkspurtscorresponding to previously-overlapped input talkspurts should remainoverlapped during playback. In some examples, the set ofperceptually-motivated rules may include a rule allowing outputtalkspurts corresponding to previously-overlapped input talkspurts to beplayed back further overlapped in time. Such a rule may be subject toone or more other rules governing the amount of permissible overlap,such as those noted in the foregoing paragraphs.

In some implementations, at least some of the conference participantspeech may be played back at a faster rate than the rate at which theconference participant speech was recoded. According to some suchimplementations, playback of the speech at the faster rate may beaccomplished by using a WSOLA (Waveform Similarity Based Overlap Add)technique. In alternative implementations, playback of the speech at thefaster rate may be accomplished by using other Time-Scale Modification(TSM) methods, such as Pitch Synchronous Overlap and Add (PSOLA) orphase vocoder methods.

FIG. 13 is a block diagram that shows an example of scheduling aconference recording for playback during an output time interval that isless than an input time interval. The types and numbers of featuresshown in FIG. 13 are merely shown by way of example. Alternativeimplementations may include more, fewer and/or different features.

In the example shown in FIG. 13, a playback scheduler 1306 is shownreceiving an input conference segment 1301 of a conference recording. Inthis example, the input time interval 1310 corresponds with a recordingtime interval of the input conference segment 1301. In FIG. 13, theinput time interval 1310 starts at input time t_(i0) and ends at inputtime t_(i1). The playback scheduler 1306 outputs a corresponding outputplayback schedule 1311, which has a smaller output time interval 1320relative to the input time interval 1310. Here, the output time interval1320 starts at output time t_(o0) and ends at output time t_(o1).

The playback scheduler 1306 may be capable of performing, at least inpart, various methods disclosed herein. For example, in someimplementations the playback scheduler 1306 may be capable ofperforming, at least in part, method 1200 of FIG. 12. The playbackscheduler 1306 may be implemented in a variety of hardware, software,firmware, etc., depending on the particular implementation. The playbackscheduler 1306 may, for example, be an instance of an element of aplayback system, such as the playback control module 605 of the playbacksystem 609 shown in FIG. 6. In alternative examples, the playbackscheduler 1306 may be implemented, at least in part, via another deviceand/or module, such as the playback control server 650 or the analysisengine 307, or may be a component of, or a module implemented via,another device, such as the control system 330 of FIG. 3A.

Accordingly, in some examples, the playback scheduler 1306 may includean interface system and a control system such as those shown in FIG. 3A.The interface system may include one or more network interfaces, one ormore interfaces between the control system and a memory system and/orone or more an external device interfaces (such as one or more universalserial bus (USB) interfaces). The control system may, for example,include a general purpose single- or multi-chip processor, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, and/or discretehardware components. In some examples, the playback scheduler 1306 maybe implemented according to instructions (e.g., software) stored onnon-transitory media. Such non-transitory media may include memorydevices such as those described herein, including but not limited torandom access memory (RAM) devices, read-only memory (ROM) devices, etc.

In the example shown in FIG. 13, the input conference segment 1301includes input talkspurts from each of endpoints 1302-1305 of an inputconference recording. In some implementations, each of the endpoints1302-1305 may correspond to a telephone endpoint, such as the telephoneendpoints 1 shown in FIG. 1A. In other implementations, each of theendpoints 1302-1305 may correspond to an in-person conference endpoint,such as the microphones 715 a-715 d shown in FIG. 7. Here, the inputconference segment 1301 includes input talkspurts 1302A-1302D fromendpoint 1302, input talkspurts 1303A-1303C from endpoint 1303, inputtalkspurts 1304A and 1304B from endpoint 1304 and input talkspurts 1305Aand 1305B from endpoint 1305.

The horizontal axes of the input conference segment 1301 and the outputplayback schedule 1311 represent time. Accordingly, the horizontaldimensions of each of the talkspurts shown in FIG. 13 correspond toexamples of talkspurt time intervals. Each input talkspurt has a starttime t start and an end time t_(end). For example, the input start timet_(start) and the input end time t_(end) of input talkspurt 1302B areshown in FIG. 13. Accordingly, according to some implementations aninput conference segment may be described as a list L_(i) of inputtalkspurts, each input talkspurt T_(i) having an input start timet_(start)(T_(i)) and an input end time t_(end)(T_(i)) and beingassociated with an endpoint.

In this example, the output playback schedule 1311 indicates a pluralityof spatial endpoint playback positions 1312-1315 and correspondingoutput talkspurts. In some implementations, each of the spatial endpointplayback positions may correspond with virtual conference participantpositions for each of the conference participants in a virtual acousticspace, e.g., as described elsewhere herein. In this example, the outputplayback schedule 1311 includes: output talkspurts 1312A-D, which areassociated with endpoint playback position 1312 and are based on inputtalkspurts 1302A-D, respectively; output talkspurts 1313A-C, which areassociated with endpoint playback position 1313 and are based on inputtalkspurts 1303A-C, respectively; output talkspurts 1314A and 1314B,which are associated with endpoint playback position 1314 and are basedon input talkspurts 1304A and 1304B, respectively; and output talkspurts1315A and 1315B, which are associated with endpoint playback position1315 and are based on input talkspurts 1305A and 1305B, respectively.

Each output talkspurt has a start time t_(start) and an end timet_(end). For example, the output start time t_(start) and the output endtime t_(end) of output talkspurt 1315A are shown in FIG. 13.Accordingly, according to some implementations an output playbackschedule may be described as a list L_(o) of output talkspurts, eachoutput talkspurt T_(o) having an output start time t_(start)(T_(o))output end time t_(end)(T_(o)) and being associated with an endpoint anda spatial endpoint playback position. Each output talkspurt also may beassociated with a corresponding input talkspurt input(T_(i)) and may bescheduled to play at output time t_(start)(T_(o)).

The playback scheduler 1306 may make the output time interval 1320smaller than the input time interval 1310 according to a variety ofmethods, depending on the particular implementation. For example, theoutput time interval 1320 may be made smaller than the input timeinterval 1310 at least in part by deleting audio data corresponding tonon-speech intervals or “gaps” between at least some of the inputtalkspurts. Some alternative implementations also may involve deletingaudio data corresponding to at least some conference participantvocalizations, such as laughter. By comparing the input conferencesegment 1301 with the output playback schedule 1311, it may be seen thatthe input talkspurts 1302A, 1302B and 1302C have gaps between them, butthat the playback scheduler 1306 has removed the gaps between thecorresponding output talkspurts 1303A-1303C.

Moreover, in the example shown in FIG. 13, at least some of theconference participant speech that did not previously overlap in time isscheduled to be played back in an overlapped fashion. For example, bycomparing the input conference segment 1301 with the output playbackschedule 1311, it may be seen that the input talkspurts 1302A and 1303Adid not previously overlap in time, but that the playback scheduler 1306has scheduled the corresponding output talkspurts 1312A and 1313A to beoverlapped in time during playback.

In this example, the playback scheduler 1306 has scheduled variousoutput talkspurts to be overlapped in time during playback according toa set of perceptually-motivated rules. In this implementation, theplayback scheduler 1306 has scheduled output talkspurts to be playedback such that two sections of speech that correspond to a singleendpoint should not overlap in time. For example, although the playbackscheduler 1306 has removed the gaps between the corresponding outputtalkspurts 1303A-1303C, all of which correspond to the endpoint 1302,the playback scheduler 1306 has not caused any of the output talkspurts1303A-1303C to overlap.

Moreover, the playback scheduler 1306 has scheduled output talkspurts tobe played back such that, given two consecutive input talkspurts A andB, A having occurred before B, the playback of an output talkspurtcorresponding to B can begin before the playback of an output talkspurtcorresponding to A is complete, but not before the playback of theoutput talkspurt corresponding to A has started. For example,consecutive input talkspurts 1302C and 1303B correspond to theoverlapping output talkspurts 1312C and 1313B. Here, the playbackscheduler 1306 has scheduled the output talkspurt 1313B to begin beforethe playback of the output talkspurt 1313C is complete, but not beforethe playback of the output talkspurt 1313C has started.

In some implementations, the playback scheduler 1306 may schedule outputtalkspurts to be played back at a speed factor S times the originalspeech rate. For example, it may be seen in FIG. 13 that the outputtalkspurts 1312A-1312D are scheduled to be played back during shortertime intervals than those of corresponding input talkspurts 1302A-1302D.In some implementations, the playback scheduler 1306 may cause theplayback of speech at a faster rate according to a WSOLA method or byusing another Time-Scale Modification (TSM) method, such as a PSOLA orphase vocoder method.

Given a list L_(i) of input talkspurts, speed factor S, overlap timet_(over) and output start time t_(o0), according to some implementationsthe playback scheduler 1306 may operate as follows. The playbackscheduler 1306 may initialize the latest input time, t_(i1), to t_(i0),the start time of the input segment. The playback scheduler 1306 mayinitialize the latest output time for each endpoint, t_(out,e), tot_(o0). The playback scheduler 1306 may initialize the output overlaptime t_(oover) to t_(o0). The playback scheduler 1306 may initialize theoutput end time t₀₁ to t_(o0). The playback scheduler 1306 mayinitialize a list L_(o) of output talkspurts to an empty list.

Each input talkspurt T_(i) may be considered in order of input starttime. In some examples, for each input talkspurt T_(i), the playbackscheduler 1306 may determine a provisional starting playback time foroutput talkspurt T_(o) for playback as follows:

$\begin{matrix}{{t_{start}^{\prime}\left( T_{o} \right)} = {\min\left( {t_{oover},{t_{o\; 1} - \frac{\max \left( {{t_{i\; 1} - {t_{start}\left( T_{i} \right)}},0} \right)}{s}}} \right)}} & \left( {{Equation}\mspace{14mu} 38} \right)\end{matrix}$

In Equation 38, t′_(start)(T_(o)) represents a provisional startingplayback time for output talkspurt T_(o), t_(start)(T_(i)) represents astart time for the input talkspurt T_(i) and S represents a speedfactor, which may be expressed as a multiple of the original speech rateat which output talkspurts are to be played back. In the example ofEquation 38, the effect of the second argument to min( ) is to maintain,in the output playback schedule 1311, the temporal relationship betweeninput talkspurt T, and the latest-finishing already-considered inputtalkspurt according to the following perceptually-motivated rules: (a)when considering two consecutive input talkspurts A and B for overlap,do not allow an output talkspurt corresponding to B to begin playbackuntil a predetermined time after playback of an output talkspurtcorresponding to A has begun; and (b) when two input talkspurts areoverlapped in input time, the corresponding output talkspurts shouldremain overlapped, having an analogous temporal relationship in outputtime.

FIG. 14 shows an example of maintaining an analogous temporalrelationship between overlapped input talkspurts and overlapped outputtalkspurts. In this example, the playback scheduler 1306 is evaluatinginput talkspurt 1402A. Accordingly, the input talkspurt 1402A is anexample of an input talkspurt T_(i). In this example, the latest-endingand already-considered input talkspurt 1401A, which overlaps in timewith the input talkspurt 1402A, ends at input time t_(i1). Here, theplayback scheduler 1306 has already scheduled the output talkspurt1401B, corresponding to the input talkspurt 1401A, to end at the outputtime t_(o1).

In FIG. 14, the output talkspurt 1402B is an example of an outputtalkspurt T_(o) corresponding with the input talkspurt T_(i). In thisexample, the playback scheduler 1306 schedules the provisional startingplayback time for the output talkspurt 1402B, according to Equation 38.By virtue of the second argument to min( ) in Equation 38, the outputtalkspurt 1402B has been scheduled to overlap 1401B by(t_(o1)−t_(start)(T_(o))), which is equal to the amount of time that theinput talkspurt 1402A overlaps the input talkspurt 1401A((t_(i1)−t_(start)(T_(i))), scaled by the speed factor S.

The playback scheduler 1306 may implement other perceptually-motivatedrules via Equation 38. One such perceptually-motivated rule may be thatgiven two consecutive input talkspurts A and B, A having occurred beforeB, the playback of the output talkspurt corresponding to B may begin nosooner than a predetermined time before the playback of the outputtalkspurt corresponding to A is complete. In some examples, thisperceptually-motivated rule may be applied even if input talkspurts Aand B did not initially overlap.

FIG. 15 shows an example of determining an amount of overlap for inputtalkspurts that did not overlap. In this implementation, the playbackscheduler 1306 is determining an output time for an output talkspurtT_(o) according to Equation 38. Here, output talkspurt 1501 is thelatest-ending output talkspurt. In this example, the block 1502Acorresponds with a provisional starting playback time for the outputtalkspurt T_(o), according to the second argument to min( ) in Equation38. However, in this example the starting playback time for the outputtalkspurt T_(o) is provisionally set to at a time t_(oover), asindicated by the block 1502B, in order to overlap output talkspurt 1501by an overlap time t_(over): in this example, due to the operation ofthe min( ) in Equation 38, t′_(start)(T_(o))=t_(oover).

The playback scheduler 1306 may implement other perceptually-motivatedrules. FIG. 16 is a block diagram that shows an example of applying aperceptually-motivated rule to avoid overlap of output talkspurts fromthe same endpoint. In this example, a playback scheduler 1306 isimplement this rule by ensuring that an output talkspurt T_(o) will notoverlap any already-scheduled output talkspurt from the same endpoint eas follows:

t _(start)(T _(o))=max(t′ _(start)(T _(o)),t _(out,e))  (Equation 39)

In the example shown in FIG. 16, by the operation of Equation 38 aninitial candidate for a starting playback time for the output talkspurtT_(o) has been set to t′_(start)(T_(o)), as shown by the position ofblock 1602A. However, in this example output talkspurt 1601 from thesame endpoint was already scheduled to be played back until timet_(out,e), which is after t′_(start)(T_(o)). Therefore, by the operationof Equation 39, the output talkspurt T_(o) is scheduled to be playedback starting at time t_(start)(T_(o)), as shown by the position ofblock 1602B.

In some examples, the output end time for output talkspurt T_(o) may becalculated as follows:

$\begin{matrix}{{t_{end}\left( T_{o} \right)} = {{t_{start}\left( T_{o} \right)} + \frac{\left( {{t_{end}\left( T_{i} \right)} - {t_{start}\left( T_{i} \right)}} \right)}{s}}} & \left( {{Equation}\mspace{14mu} 40} \right)\end{matrix}$

In the example of Equation 40, t_(end)(T_(o)) represents the output endtime for the output talkspurt T_(o). In this example, the time intervalduring which the output talkspurt T_(o) is scheduled to be played backis reduced by dividing the input talkspurt time interval(t_(end)(T_(i))−t_(start)(T_(i))) by the speed factor S.

In some implementations, the output talkspurt T_(o) may then be appendedto output talkspurt list L_(o). In some examples, the latest output timefor the endpoint e of talkspurt T_(o) may be updated according to:

t _(out,e) =t _(end)(T _(o))  (Equation 41)

In some examples, the output overlap time may be updated according to:

t _(oover)=max(t _(oover) ,t _(end)(T _(o))−t _(over))  (Equation 42)

According to some implementations, the latest input end time may beupdated according to:

t _(i1)=max(t _(i1) ,t _(start)(T _(i)))  (Equation 43)

In some instances, the latest output end time may be updated accordingto:

t _(o1)=max(t _(o1) ,t _(end)(T _(o)))  (Equation 44)

The foregoing process may be repeated until all input talkspurts havebeen processed. The scheduled output list L_(o) may then be returned.

Some conferences may involve presentations by multiple conferenceparticipants. As used herein, a “presentation” may correspond to anextended time interval (which may, for example, be several minutes ormore) during which a single conference participant is the primaryspeaker or, in some instances, the only speaker. In someimplementations, the set of perceptually-motivated rules may include arule allowing the concurrent playback of entire presentations fromdifferent conference participants. According to some suchimplementations, at least some of the conference participant speech maybe played back at a faster rate than the rate at which the conferenceparticipant speech was recorded.

FIG. 17 is a block diagram that shows an example of a system capable ofscheduling concurrent playback of entire presentations from differentconference participants. The types and numbers of features shown in FIG.17 are merely shown by way of example. Alternative implementations mayinclude more, fewer and/or different features.

In the example shown in FIG. 17, the system 1700 includes a segmentscheduler unit 1710, which is shown receiving a segmented conferencerecording 1706A. In some examples, the segmented conference recording1706A may be segmented according to conversational dynamic data, toallow discussions, presentations and/or other types of conferencesegments to be identified. Some examples of conference segmentationaccording to conversational dynamic data are provided below. In thisexample, the segmented conference recording 1706A includes thediscussion segment 1701A, followed by the presentation segments1702A-1704A, followed by the discussion segment 1705A.

The segment scheduler unit 1710 and the other elements of system 1700may be capable of performing, at least in part, various methodsdisclosed herein. For example, in some implementations the segmentscheduler unit 1710 and the other elements of system 1700 may be capableof scheduling segments of a segmented conference recording forconcurrent playback of presentations from different conferenceparticipants. The segment scheduler unit 1710 and the other elements ofsystem 1700 may be implemented in a variety of hardware, software,firmware, etc., depending on the particular implementation. For example,the segment scheduler unit 1710 and/or the other elements of system 1700may be implemented via a general purpose single- or multi-chipprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,and/or discrete hardware components. In some examples, the segmentscheduler unit 1710 and/or the other elements of system 1700 may beimplemented according to instructions (e.g., software) stored onnon-transitory media. Such non-transitory media may include memorydevices such as those described herein, including but not limited torandom access memory (RAM) devices, read-only memory (ROM) devices, etc.The segment scheduler unit 1710 and/or the other elements of system 1700may, for example, be components of the playback system 609, such as theplayback control module 605 shown in FIG. 6. In alternative examples,the segment scheduler unit 1710 and/or the other elements of system 1700may be implemented in another device or module, such as the playbackcontrol server 650 or the analysis engine 307, or may be implemented bya component of another device or module, such as the control system 330of FIG. 3A.

In the example shown in FIG. 17, segment scheduler unit 1710 is capableof determining whether there are consecutive presentation segments, eachpresented by a different presenter, that can be played in parallel.Here, the result of this process is the segment schedule 1706B. In thisimplementation, the segment schedule 1706B includes a discussion segment1701B, which is based on the discussion segment 1701A and which will beplayed first, by itself. Here, the segment schedule 1706B includespresentation segments 1702B-1704B, which are based on the presentationsegments 1702A-1704A, respectively. The presentation segments1702B-1704B will be played concurrently and after the discussion segment1701B in this implementation.

In this example, the interjection filtering modules 1702C-1704C arecapable of removing interjections from the presentation segments1702B-1704B. Here, the interjections are talkspurts that are not speechof a “presenter,” a conference participant who is making a presentation.In some implementations, interjections may not be removed from apresentation segment, e.g., if the presentation segment is not scheduledto be played in parallel with another presentation segment. Accordingly,the interjection filtering modules 1702C-1704C may ensure that speechfrom the same endpoint is not played concurrently.

In this implementation, the system 1700 includes a playback schedulerunit 1306, such as that shown in FIG. 13. Here, playback scheduler unit1306 includes modules 1701D-1705D, each of which is capable ofindependently scheduling one of the conference segments for playback.The modules 1701D and 1705D receive discussion segments 1701B and 1705B,respectively, and output corresponding discussion playback schedules1701F and 1705F. The modules 1702D-1704D receive output from theinterjection filtering modules 1702C-1704C, corresponding topresentation segments 1702B-1704B, and output corresponding independentpresentation playback schedules. In some alternative implementations, aseparate instance of the playback scheduler unit 1306 may be created foreach segment. In some implementations, each segment may be passed to ascheduler function in turn, so that the scheduling process starts afreshfor each segment.

In this example, the system 1700 also includes a merging unit 1702E.Here, the merging unit 1702E is capable of merging playback schedules(in output time) for segments that are to be played concurrently into asingle playback schedule. In this implementation, the modules1702D-1704D provide independent presentation playback schedulescorresponding to presentation segments 1702B-1704B to the merging unit1702E, which outputs a merged presentation playback schedule 1702F. Inthis example, the merged presentation playback schedule 1702F has alength equal to the maximum length of any of the input schedules.

In the implementation shown in FIG. 17, the system 1700 includes aconcatenation unit 1706G. In this example, the concatenation unit 1706Gis capable of concatenating the first discussion playback schedule1701F, the merged presentation playback schedule 1702F and the seconddiscussion playback schedule 1705F, and of outputting a single outputplayback schedule 1706H.

According to some implementations of the segment scheduler unit 1710,the output schedule 1076H may be initialized to an empty list. Thescheduler unit 1710 may process each of the segments of a conferencerecording in order, considering each segment in turn. When the segmentunder consideration is not a presentation segment, it may be scheduledto produce a segment schedule (e.g., 1701F) and then concatenated to theoutput playback schedule 1076H with an appropriate output time offset,so that the segment is scheduled to start after the last talkspurtcurrently in the output playback schedule 1076H. The segment schedulerunit 1710 may then continue with the next segment.

When the segment under consideration is a presentation schedule, thesegment scheduler unit 1710 also may consider following segments as longas they are presentations from different presenters. Once a run ofpresentation segments that may be played back in parallel has beendiscovered, each of the presentation segments may be filtered forinterjections and then separately scheduled using the playback scheduler605. The merging unit 1702E may then merge the schedules from each ofthe presentation segments by combining all of the corresponding outputtalkspurts into a single list that is sorted by output start time. Theconcatenation unit 1706G may then concatenate the merged presentationschedule to the output schedule 1076H with an appropriate output timeoffset so that they start after the last talkspurt content currently inthe output schedule. The segment scheduler unit 1710 may then continuewith the next segment.

It is often difficult for a listener to find regions of interest in aconference recording without listening to the entire recording. This isparticularly true if the listener did not attend the conference. Thepresent disclosure introduces various novel techniques to aid a listenerin finding regions of interest within a conference recording.

Various implementations described herein involve dividing a conferencerecording into different segments based on the class of humaninteraction that seems to predominantly occur in each segment. Thesegments may correspond with a time interval and at least one segmentclassification corresponding with a class of human interaction. Forexample, if from time T1 to time T2, conference participant A seems tohave been giving a presentation, a “Presentation” segment may beidentified in the time interval from time T1 to time T2. ThePresentation segment may be associated with conference participant A. Ifconference participant A seems to have been answering questions from hisor her audience from time T2 to time T3, a “Question and Answer” or“Q&A” segment may be identified in the time interval from time T2 totime T3. The Q&A segment may be associated with conference participantA. If conference participant A seems to have been involved in adiscussion with other conference participants during the remainder ofthe conference recording following time T3, a “Discussion” segment maybe identified in the time interval after time T3. The Discussion segmentmay be associated with the conference participants involved in thediscussion.

The resulting segmentation of a conference recording may be potentiallyuseful in a variety of ways. Segmentation can supplement content-basedsearch techniques such as keyword spotting and/or topic determination.For example, instead of searching for the term “helicopter” in an entire3-hour conference recording, some implementations may allow a listenerto search for the term “helicopter” in a particular 30-minutepresentation from a particular conference participant within thatrecording. The ability to further refine a search in this manner canreduce the time it takes to find a particular region and/or event ofinterest in a teleconference recording.

Some playback system implementations disclosed herein provide agraphical user interface, which may include a visual depiction ofconference segments. In such implementations, the visual depiction ofconference segments may be useful for providing a visual overview to theuser of the playback system of the events of a conference. This visualoverview may aid the user in browsing through the conference content.For example, some implementations may allow a listener to browse throughall discussion segments and/or all discussion segments that involved aparticular conference participant.

Moreover, such conference segmentation may be useful in downstreamannotation and search techniques. For example, once the meeting has beenbroken down into segments based on conversational dynamics, it may bepossible to indicate to the user an idea of what topic was coveredduring that segment by making use of automatic speech recognition. Forexample, the listener may want to browse through all presentationsegments or discussion segments involving a particular topic.

FIG. 18A is a flow diagram that outlines one example of a conferencesegmentation method. In some examples, method 1800 may be performed byan apparatus, such as the apparatus of FIG. 3A and/or one or morecomponents of the analysis engine 307 of FIG. 1A or FIG. 3C.

In some implementations, the method 1800 may be performed by at leastone device according to software stored on one or more non-transitorymedia. The blocks of method 1800, like other methods described herein,are not necessarily performed in the order indicated. Moreover, suchmethods may include more or fewer blocks than shown and/or described.

In this implementation, block 1805 involves receiving audio datacorresponding to a recording of a conference involving a plurality ofconference participants. In this example, the audio data includes: (a)conference participant speech data from multiple endpoints, recordedseparately; and/or (b) conference participant speech data from a singleendpoint corresponding to multiple conference participants.

In some implementations, the audio data may include output of a voiceactivity detection process. Accordingly, in some such implementationsthe audio data includes indications of speech and/or non-speechcomponents. However, if the audio data does not include output of avoice activity detection process, in some examples method 1800 mayinvolve a voice activity detection process.

According to the example shown in FIG. 18A, conference participantspeech data from a single endpoint that corresponds to multipleconference participants also includes information for identifyingconference participant speech for each conference participant of themultiple conference participants. Such information may be output from aspeaker diarization process. However, if the audio data does not includeoutput from a speaker diarization process, in some examples method 1800may involve a speaker diarization process.

In some implementations, in block 1805 a control system, such as thecontrol system 330 of FIG. 3A, may receive the audio data via theinterface system 325. In some examples, the control system may becapable of performing blocks 1805-1820 of method 1800. In someimplementations, the control system may be capable of performing othersegmentation-related methods disclosed herein, such as those describedherein with reference to FIGS. 18B-23. In some examples, method 1800 maybe performed, at least in part, by one or more components of the jointanalysis module 306, such as the conversational dynamics analysis module510 of FIG. 5. According to some such implementations, block 1805 mayinvolve receipt of the audio data by the conversational dynamicsanalysis module 510.

In some implementations, the conference may be a teleconference, whereasin other implementations the conference may be an in-person conference.According to some examples, the audio data may correspond to a recordingof a complete or a substantially complete conference.

In this example, block 1810 involves analyzing the audio data todetermine conversational dynamics data. In some instances, theconversational dynamics data may include data indicating the frequencyand duration of conference participant speech, doubletalk dataindicating instances of conference participant doubletalk during whichat least two conference participants are speaking simultaneously, etc.In some implementations, block 1810 may involve determining a doubletalkratio, which may indicate a fraction of speech time, in a time interval,during which at least two conference participants are speakingsimultaneously.

Some implementations described herein involve evaluating analyzing theaudio data to determine other types of conversational dynamics data. Forexample, in some implementations the conversational dynamics datadetermined in block 1810 may include a speech density metric indicatinga fraction of the time interval during which there is any conferenceparticipant speech. In some implementations, block 1810 may involvedetermining a dominance metric indicating a fraction of total speechuttered by a dominant conference participant during the time interval.The dominant conference participant may, for example, be a conferenceparticipant who spoke the most during the time interval.

In this implementation, block 1815 involves searching the conferencerecording to determine instances of each of a plurality of segmentclassifications. In this example, each of the segment classifications isbased, at least in part, on the conversational dynamics data. Variousexamples are described below.

In some implementations, block 1815 may involve determining instances ofBabble segments, which are segments during which at least two conferenceparticipants are talking concurrently. In some examples, Babble segmentsmay be identified according to instances of doubletalk data, such asinstances of doubletalk that continue during a threshold time intervaland/or a fraction of a time interval during which there is doubletalk.Babble segments are often found at the start of a conference,particularly a conference that includes at least one multi-partyendpoint, before a substantive discussion, presentation, etc.

According to some implementations, block 1815 may involve determininginstances of Mutual Silence segments, which are time intervals duringwhich there is a negligible amount (e.g., less than a mutual silencethreshold amount) of speech. This may occur, for example, inteleconferences when one conference participant temporarily leaves hisor her endpoint unattended while others await his or her return and/orwhen one conference participant is waiting for others to join ateleconference. In some implementations, Mutual Silence segments may bebased, at least in part on a speech density metric, which may bedetermined in block 1810.

Due in part to their distinctive conversational dynamicscharacteristics, instances of Babble segments may be identified with ahigh level of confidence and instances of Mutual Silence segments may beidentified with a very high level of confidence. Moreover, the starttimes and end times of Babble segments and Mutual Silence segments maybe identified with a relatively high level of confidence. Because thereis a relatively low likelihood that a Babble segment includesintelligible speech corresponding to a conference topic of interest anda very low likelihood that a Mutual Silence segment includes any speechcorresponding to a conference topic of interest, a person reviewing theconference recording may be reasonably confident that he or she maysafely omit review of such conference segments. Therefore, identifyingBabble segments and Mutual Silence segments can result in time savingsto a listener during playback of a conference recording.

In some implementations, block 1815 may involve determining instances ofPresentation segments, which are segments during which one conferenceparticipant is doing the vast majority of the talking, while otherconference participants remain substantially silent. According to someimplementations, determining instances of Presentation segments may bebased, at least in part, on a speech density metric and a dominancemetric. Presentations generally involve very little doubletalk.Therefore, in some implementations determining instances of Presentationsegments may be based, at least in part, on a doubletalk metric, such asa doubletalk ratio.

Due in part to their distinctive conversational dynamicscharacteristics, instances of Presentation segments may be identifiedwith a relatively high level of confidence. In some implementations, thestart times and end times of Presentation segments may be identifiedwith a reasonably high level of confidence, but generally with a lowerlevel of confidence than that with which the start times and end timesof Babble segments and Mutual Silence segments may be identified.Because there is a high likelihood that a Presentation segment includesspeech corresponding to a conference topic of interest, it may beadvantageous to a reviewer to have such conference segments identified.Such potential advantages may be enhanced in implementations whichprovide additional information regarding conference segments, such asimplementations which involve keyword identification, topicdetermination, etc. For example, a listener may choose to review onlyPresentation segments in which a particular word was uttered or duringwhich a particular topic is discussed. Accordingly, identifyingPresentation segments can result in time savings to a listener duringplayback of a conference recording.

In some implementations, block 1815 may involve determining instances ofDiscussion segments, which are segments during which multiple conferenceparticipants speak, but without any clear dominance from a singleconference participant. According to some implementations, determininginstances of Discussion segments may be based, at least in part, on aspeech density metric and a dominance metric. Some discussions mayinvolve a significant amount of doubletalk, but usually not as muchdoubletalk as a Babble segment. Therefore, in some implementationsdetermining instances of Discussion segments may be based, at least inpart, on a doubletalk metric, such as a doubletalk ratio.

In some implementations, block 1815 may involve determining instances ofQ&A segments, which are segments that correspond with a time intervalduring which multiple conference participants ask questions and either asingle conference participant replies or one participant replies from asmaller subset of conference participants. For example, a Q&A segmentoften may follow the conclusion of a presentation segment. After thepresentation, the presenting conference participant may answer questionsposed by other conference participants who were listening to thepresentation. During question and answer sessions, a single conferenceparticipant often replies, so that conference participant may do moretalking than any other conference participant. Accordingly, thedominance metric may be less than that for a presentation and greaterthan that for a discussion. Therefore, according to someimplementations, determining instances of Q&A segments may be based, atleast in part, on a speech density metric and a dominance metric. Theremay sometimes be a significant amount of doubletalk during a questionand answer session (e.g., more doubletalk than there is during apresentation), but there may be less doubletalk during a question andanswer session than during a discussion. Accordingly, in someimplementations determining instances of Q&A segments may be based, atleast in part, on a doubletalk metric, such as a doubletalk ratio.

In some implementations, Discussion segments and Q&A segments may not beidentified with the same level of confidence as, for example, a MutualSilence segment, a Babble segment or even a Presentation segment. Insome implementations, the start times and end times of Discussionsegments and Q&A segments may be identified with a moderate level ofconfidence, but generally with a lower level of confidence than thatwith which the start times and end times of Babble segments and MutualSilence segments may be identified. However, because there is areasonable likelihood that a Discussion segment or a Q&A segment mayinclude speech corresponding to a conference topic of interest, it maybe advantageous to a reviewer to have such conference segmentsidentified. Such potential advantages may be enhanced in implementationswhich provide additional information regarding conference segments, suchas implementations which involve keyword identification, topicdetermination, etc. For example, a listener may choose to review onlyPresentation segments, Discussion segments and/or Q&A segments in whicha particular word was uttered or during which a particular topic isdiscussed. Accordingly, identifying Discussion segments and/or Q&Asegments can result in time savings to a listener during playback of aconference recording.

Here, block 1820 involves segmenting the conference recording into aplurality of segments. In this example, each of the segments correspondswith a time interval and at least one of the segment classifications. Asegment may correspond with additional information, such as theconference participant(s), if any, who speak during the segment.

According to some implementations, the searching and/or segmentingprocesses may be recursive. In some implementations, the analyzing,searching and segmenting processes may all be recursive. Variousexamples are provided below.

In the following description, it may be observed that several of thesearch processes may involve temporal thresholds (such as t_(min) andt_(snap)), which will be described below. These temporal thresholds havethe effect of limiting the size of a segment to be not smaller than athreshold time. According to some implementations, when the results of asegmentation process are displayed to a user (for example, when theplayback system 609 of FIG. 6 causes a corresponding graphical userinterface to be provided on a display), the user may be able to zoom inand out in time (for example, by interacting with a touch screen, byusing a mouse or by activating zoom in or zoom out commands). In such asituation, it may be desirable to have performed the segmentationprocess multiple times at different timescales (which may involveapplying different values of t_(min) and t_(snap)). During playback, itmay be advantageous to switch dynamically between segmentation resultsat different time scales, the results of which may be displayed to theuser based on the current zoom level. According to some examples, thisprocess may involve choosing a segmentation timescale that will notcontain segments that occupy less than X pixels in width at the currentzoom level. The value of X may be based, at least in part, on theresolution and/or size of the display. In one example, X may equal 100pixels. In alternative examples, X may equal 50 pixels, 150 pixels, 200pixels, 250 pixels, 300 pixels, 350 pixels, 400 pixels, 450 pixels, 500pixels, or some other number of pixels. The conversational dynamics datafiles 515 a-515 e, shown in FIG. 5, are examples of segmentation resultsat different time scales that may be used for quickly adjusting adisplay based on the current zoom level.

However, in other implementations blocks 1810-1820 may not be performedrecursively, but instead may each be performed a predetermined number oftimes, such as only one time, only two times, etc. Alternatively, oradditionally, in some implementations blocks 1810-1820 may be performedat only one time scale. The output of such implementations may not be asaccurate or as convenient for a listener as recursive processes.However, some such implementations may be performed more rapidly thanrecursive implementations and/or implementations performed for multipletime scales. Alternatively, or additionally, such implementations may besimpler to implement than recursive implementations and/orimplementations performed for multiple time scales.

In some implementations, the searching and segmenting processes (and, insome implementations, the analyzing process) may be based, at least inpart, on a hierarchy of segment classifications. According to someimplementations, the analyzing, searching and segmenting processes allmay be based, at least in part, on a hierarchy of segmentclassifications. As noted above, different segment types, as well as thestart and end times for different segment types, may be identified withvarying degrees of confidence. Therefore, according to someimplementations, the hierarchy of segment classifications is based, atleast in part, upon a level of confidence with which segments of aparticular segment classification may be identified, a level ofconfidence with which a start time of a segment may be determined and/ora level of confidence with which an end time of a segment may bedetermined.

For example, a first or highest level of the hierarchy of segmentclassifications may correspond with Babble segments or Mutual Silencesegments, which may be identified with a high (or very high) level ofconfidence. The start and end times of Babble segments and MutualSilence segments also may be determined with a high (or very high) levelof confidence. Accordingly, in some implementations a first stage of thesearching and segmenting processes (and, in some implementations, theanalyzing process) may involve locating Babble segments or MutualSilence segments.

Moreover, different segment types have different likelihoods ofincluding subject matter of interest, such as conference participantspeech corresponding to a conference topic, a keyword of interest, etc.It may be advantageous to identify which conference segments can beskipped, as well as which conference segments are likely to includesubject matter of interest. For example, Babble segments and MutualSilence segments have a low or very low likelihood of includingconference participant speech corresponding to a conference topic, akeyword of interest, etc. Presentation segments may have a highlikelihood of including conference participant speech corresponding to aconference topic, a keyword of interest, etc. Therefore, according tosome implementations, the hierarchy of segment classifications is based,at least in part, upon a likelihood that a particular segmentclassification includes conference participant speech corresponding to aconference topic.

According to some implementations, the searching and segmentingprocesses (and, in some implementations, the analyzing process) mayinvolve locating Babble segments first, then Presentation segments, thenQ&A segments, then other segments. The processes may be recursiveprocesses. Other implementations may involve locating segments in one ormore different sequences.

FIG. 18B shows an example of a system for performing, at least in part,some of the conference segmentation methods and related methodsdescribed herein. As with other figures provided herein, the numbers andtypes of elements shown in FIG. 18B are merely shown by way of example.In this example, audio recordings 1801A-1803A are being received byspeaker diarization units 1801B-1803B. The audio recordings 1801A-1803Amay, in some implementations, correspond with the packet trace files201B-205B described above with reference to FIGS. 3C and 4, each ofwhich may correspond to one of the uplink data packet streams 201A-205A.The speaker diarization units 1801B-1803B may, in some implementations,be instances of the speaker diarization module 407 shown in FIG. 4.

In this example, each of the audio recordings 1801A-1803A is from atelephone endpoint. Here, audio recording 1801A is a recording from amulti-party endpoint (e.g., a speakerphone), while audio recordings1802A and 1803A are recordings of single-party endpoints (e.g. standardtelephones and/or headsets).

In this example, the speaker diarization units 1801B-1803B are capableof determining when speech was uttered by each conference participant.When processing audio data from a single-party endpoint, such as theaudio recordings 1802B and 1803B, the speaker diarization units 1802Band 1803B may function as a voice activity detector. When processingaudio data from a multi-party endpoint, such as the audio recording1801A, the speaker diarization unit 1801C may estimate how manyconference participants are present (e.g., how many conferenceparticipants are speaking during the conference) and may attempt toidentify which of the conference participants uttered each talkspurt. Insome implementations, the speaker diarization units 1801B-1803B may usemethods known by those of ordinary skill in the art. For example, insome implementations the speaker diarization units 1801B-1803B may use aGaussian mixture model to model each of the talkers and may assign thecorresponding talkspurts for each talker according to a Hidden Markovmodel.

In the implementation shown in FIG. 18B, the speaker diarization units1801B-1803B output the speaker activity documents 1801C-1803C. Here,each of the speaker activity documents 1801C-1803C indicates when speechwas uttered by each conference participant at a corresponding endpoint.The speaker activity documents 1801C-1803C may, in some implementations,be instances of the uplink analysis results available for joint analysis401-405 shown in FIG. 5.

In this example, the speaker activity documents 1801C-1803C are receivedby the segmentation unit 1804 for further processing. The segmentationunit 1804 produces a segmentation record 1808 that is based, at least inpart, on the speaker activity documents 1801C-1803C. The segmentationunit 1804 may, in some implementations, be an instance of theconversational dynamics analysis module 510 of FIG. 5. In some suchimplementations, the segmentation record 1808 may be an instance of oneof the conversational dynamics data files 515 a-515 e that are shown tobe output by the conversational dynamics analysis module 510 in FIG. 5.

The segmentation unit 1804 and the speaker diarization units 1801B-1803Bmay, depending on the particular example, be implemented via hardware,software and/or firmware, e.g., via part of a control system that mayinclude at least one of a general purpose single- or multi-chipprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic, ordiscrete hardware components. In some examples, the segmentation unit1804 and the speaker diarization units 1801A-1803B may be implementedaccording to instructions (e.g., software) stored on non-transitorymedia, such as random access memory (RAM) devices, read-only memory(ROM) devices, etc.

In this example, the segmentation unit 1804 includes a merge unit 1806,which is capable of combining the plurality of speaker activitydocuments 1801C-1803C into a global speaker activity map 1809. A globalspeaker activity map 1809 for the time interval from t₀ to t₁, whichcorresponds to an entire conference in this example, is shown in FIG.18B. The global speaker activity map 1809 indicates which conferenceparticipants spoke during which time intervals and at which endpointduring the conference.

In this example, the segmentation unit 1804 includes a segmentationengine 1807, which is capable of performing analyzing, searching andsegmenting processes such as those described above with reference toFIG. 18A. The analyzing, searching and segmenting processes maysometimes be collectively referred to herein as a “segmentationprocess.” In this implementation, the segmentation engine 1807 iscapable of performing a hierarchical and recursive segmentation process,starting with a process of locating Babble segments. In alternativeimplementations, the segmentation engine 1807 may start with a processof locating another classification of segment, such as Mutual Silence orPresentation segments.

In this example, the segmentation record 1808 is a list of segments1808A-1808F found in the conference. Here, each of the segments1808A-1808F has a start time, an end time and a segment classificationidentifier. In this example, the segment classification identifier willindicate that the segment is a Mutual Silence segment, a Babble segment,a Presentation segment, a Discussion segment or a Question and Answer(Q&A) segment. Other implementations may involve more or fewer segmentclassifications. In this example, the segments 1808A and 1808F areBabble segments, the segments 1808B and 1808D are Presentation segments,the segment 1808C is a Q&A segment and the segment 1808E is a Discussionsegment.

FIG. 19 outlines an initial stage of a segmentation process according tosome implementations disclosed herein. According to some suchimplementations, all stages of the segmentation process may beperformed, at least in part, by the segmentation engine 1807 of FIG.18B. In this example, the segmentation engine 1807 is capable ofperforming a recursive segmentation process starting with a “MakeBabble” process 1901. In this example, a function call has been made toa subroutine that includes instructions for the Make Babble process1901. Here, the Make Babble process 1901 produces a partial segmentationrecord 1903A containing one or more Babble segments or a partialsegmentation record 1903B containing no Babble segments, depending onthe results of the Make Babble process 1901.

Here, because this is the first and highest-level part of thesegmentation process, the speaker activity map input to the Make Babbleprocess 1901 is the global speaker activity map 1809, which indicatesspeaker activity for the entire conference. Accordingly, in this examplethe time interval between times t₀ and t₁ includes the entireconference. However, in other examples the Make Babble process 1901 mayreceive a speaker activity map having a smaller time interval in orderto generate partial segmentation records corresponding to a smaller timescale.

In this example, the Make Babble process 1901 includes a longest Babblesegment search process 1904. In this example, the longest Babble segmentsearch process 1904 is capable of searching the global speaker activitymap 1809 to locate the longest Babble segment between times t₀ and t₁.If no suitable Babble segment can be located, the partial segmentationrecord 1903B containing no Babble segments is passed down to a MakePresentation process 2001, which is described below with reference toFIG. 20.

In this example, however, the longest Babble segment search process 1904locates a longest Babble segment 1906B1, having start time t₂ and endtime t₃, which is entered into the partial segmentation record 1903A.Here, the preceding speaker activity map 1906A is the remainingun-segmented portion of the input global speaker activity map 1809during the time interval preceding that of the longest Babble segment1906B1 (from time t₀ to time t₂). In this example, the subsequentspeaker activity map 1906C is the remaining un-segmented portion of theinput global speaker activity map 1809 during the time intervalfollowing the longest Babble segment 1906B1 (from time t₃ to time t₁).The preceding speaker activity map 1906A and the subsequent speakeractivity map 1906C may be provided as input to one or more subsequentrecursions of the Make Babble process 1901.

According to some implementations, however, the time intervals of thepreceding speaker activity map 1906A and the subsequent speaker activitymap 1906C may be evaluated to determine whether they are shorter than athreshold t_(snap). If, for example, the time interval of the precedingspeaker activity map 1906A is determined to be shorter than a thresholdt_(snap), the longest Babble segment 1906B1 will be “snapped” to spanthe time interval of the preceding speaker activity map 1906A by lettingt₂=t₀. Otherwise, the preceding speaker activity map 1906A is input tothe preceding speaker activity recursion 1907A. According to some suchimplementations, if the time interval of the subsequent speaker activitymap 1906C is shorter than the threshold t_(snap), the longest Babblesegment 1906B1 will be “snapped” to span the time interval of thesubsequent speaker activity map 1906C by letting t₃=t₁. Otherwise, thesubsequent speaker activity map 1906C is input to the subsequent speakeractivity recursion 1907C.

In the example shown in FIG. 19, the time intervals of the precedingspeaker activity map 1906A and the subsequent speaker activity map 1906Care both longer than the threshold t_(snap). Here, the preceding speakeractivity recursion 1907A outputs a preceding partial segmentation record1908A, which includes additional Babble segments 1906B2 and 1906B3,which are shown in FIG. 19 with the same type of fill as that of thelongest Babble segment 1906B1. In this example, the subsequent speakeractivity recursion 1907C outputs a subsequent partial segmentationrecord 1908C, which includes additional instances of Babble segments.These Babble segments are also shown in FIG. 19 with the same type offill as that of the longest Babble segment 1906B1. In this example, thepreceding partial segmentation record 1908A, the longest Babble segment1906B1 and the subsequent partial segmentation record 1908C areconcatenated to form the partial segmentation record 1903A.

According to some implementations, in order to initiate the longestBabble segment search process 1904, a list of doubletalk segments may bemade. For example, list of doubletalk segments may be made in descendingorder of doubletalk segment length. A doubletalk segment is a segment ofthe conference that includes an instance of doubletalk, during which atleast two conference participants are talking concurrently. Each ofthese doubletalk segments may be considered in turn (e.g., in descendingorder of length) as a root candidate Babble segment and the longestBabble segment search process 1904 may proceed for each. The longestBabble segment found starting from any root candidate is returned. In analternative embodiment, the search may proceed from each root candidatein turn until any one of them returns a valid Babble segment. The firstBabble segment found may be returned and the search may terminate. Witheither type of implementation, if no Babble segment is found aftersearching through each root candidate, then the longest Babble segmentsearch process 1904 may report that no Babble segment can be found,e.g., by outputting a partial segmentation record 1903B containing noBabble segments.

In some implementations, in order to be included in a candidate Babblesegment, a talkspurt must be at least a threshold candidate segment timeinterval in duration (e.g., 600 ms long, 700 ms long, 800 ms long, 900ms long, 1 second long, etc.) and must be classified as Babble (e.g.,according to a determination of the classifier 2301 shown in FIG. 22).According to some examples, a candidate Babble segment may be classifiedas Babble according to a metric referred to herein as the “babble rate,”which may be defined as the fraction of time within the candidatesegment during which there is doubletalk. For example, for a candidateBabble segment starting at time 50 and ending at time 54 (4 secondslong), with a single talkspurt from time 51 to 53 classified as Babble(2 seconds long), the babble rate is 50%. Some such examples may requirethat a candidate Babble segment have at least a threshold babble rate(e.g., 40%, 45%, 50%, 55%, 60%, etc.) in order to be classified as aBabble segment.

Some implementations disclosed herein may make a distinction between thebabble rate and a “doubletalk ratio,” which is discussed in more detailbelow. In some such implementations, the doubletalk ratio is thefraction of speech time within a time interval (as opposed to the totaltime duration of the time interval) corresponding to the candidatesegment during which there is double talk.

According to some implementations, the next Babble talkspurt that is atleast the threshold candidate segment time in duration may be added tothe previous candidate Babble segment to form one new candidate Babblesegment. In some examples, the next Babble talkspurt must be within athreshold candidate segment time interval of the previous candidateBabble segment in order to be added to the previous candidate Babblesegment.

Likewise, the previous Babble talkspurt that is at least the thresholdcandidate segment time interval in duration may be added to the previouscandidate Babble segment to form a second new candidate Babble segment.In some examples, the previous Babble talkspurt must be within athreshold candidate segment time interval of the previous candidateBabble segment in order to be added to the previous candidate Babblesegment. Thus, according to such implementations, zero, one or twocandidate Babble segments may be generated at each step.

In alternative implementations, such as that described below withreference to FIG. 23, the next Babble talkspurt may be evaluated in onestep and then the previous Babble talkspurt may be evaluated in a secondstep. According to such implementations, zero or one candidate Babblesegments may be generated at each step.

FIG. 20 outlines a subsequent stage of a segmentation process accordingto some implementations disclosed herein. In this example, a functioncall has been made to a subroutine that includes instructions for theMake Presentation process 2001. According to some implementations, theMake Presentation process 2001 may be similar to the Make Babble process1901. Here, the Make Presentation process 2001 produces a partialsegmentation record 2003A containing one or more Presentation segmentsor a partial segmentation record 2003B containing no Presentationsegments, depending on the results of the Make Presentation process2001.

The input speaker activity map 2002 to the Make Presentation process2001 may depend on the particular implementation. In someimplementations, the input speaker activity map 2002 may be the globalspeaker activity map 1809, which indicates speaker activity for theentire conference, or a speaker activity map corresponding to a smallertime interval. However, in some implementations the Make Presentationprocess 2001 may receive input from the Make Babble process indicatingwhich time intervals of the conference (or which time intervals of aportion or the conference) correspond to Babble segments. According tosome such implementations, the input speaker activity map 2002 maycorrespond to a time interval that does not correspond to Babblesegments.

In this example, the Make Presentation process 2001 includes a longestPresentation segment search process 2004. In this example, the longestPresentation segment search process 2004 is capable of searching theinput speaker activity map 2002 to locate the longest Presentationsegment between times t₀ and t₁. If no suitable Presentation segment isfound, the segmentation process may continue to a subsequent process,such as the Make Other process 2101, which is described below withreference to FIG. 21.

In this example, however, the longest Presentation segment searchprocess 2004 locates a longest Presentation segment 2006B1, having starttime t₂ and end time t₃, which is entered into the partial segmentationrecord 2003A. Here, the preceding speaker activity map 2006A is theremaining un-segmented portion of the input global speaker activity map1809 during the time interval preceding that of the longest Presentationsegment 2006B1 (from time t₀ to time t₂). In this example, thesubsequent speaker activity map 2006C is the remaining un-segmentedportion of the input global speaker activity map 1809 during the timeinterval following the longest Presentation segment 2006B1 (from time t₃to time t₁). The preceding speaker activity map 2006A and the subsequentspeaker activity map 2006C may be provided as input to one or moresubsequent recursions of the Make Presentation process 2001.

According to some implementations, however, the time intervals of thepreceding speaker activity map 2006A and the subsequent speaker activitymap 2006C may be evaluated to determine whether they are shorter than athreshold t_(snap). If, for example, the time interval of the precedingspeaker activity map 2006A is determined to be shorter than a thresholdt_(snap), the longest Presentation segment 2006B1 will be “snapped” tospan the time interval of the preceding speaker activity map 2006A byletting t₂=t₀. Otherwise, the preceding speaker activity map 2006A isinput to the preceding speaker activity recursion 2007A. According tosome such implementations, if the time interval of the subsequentspeaker activity map 2006C is shorter than the threshold t_(snap), thelongest Presentation segment 2006B1 will be “snapped” to span the timeinterval of the subsequent speaker activity map 2006C by letting t₃=t₁.Otherwise, the subsequent speaker activity map 2006C is input to thesubsequent speaker activity recursion 2007C.

In the example shown in FIG. 20, the time intervals of the precedingspeaker activity map 2006A and the subsequent speaker activity map 2006Care both longer than the threshold t_(snap). Here, the preceding speakeractivity recursion 2007A outputs a preceding partial segmentation record2008A, which includes additional Presentation segments 2006B2 and2006B3, which are shown in FIG. 20 with the same type of fill as that ofthe longest Presentation segment 2006B1. In this example, the subsequentspeaker activity recursion 2007C outputs a subsequent partialsegmentation record 2008C, which includes additional instances ofPresentation segments. These Presentation segments are also shown inFIG. 20 with the same type of fill as that of the longest Presentationsegment 2006B1. In this example, the preceding partial segmentationrecord 2008A, the longest Presentation segment 2006B1 and the subsequentpartial segmentation record 2008C are concatenated to form the partialsegmentation record 2003A.

In some examples, when searching for Presentation segments, each rootcandidate segment may be a segment corresponding to an individualtalkburst. Searching may begin at each root candidate segment in turn(for example, in descending order of length) until all root candidatesare searched and the longest presentation returned.

In an alternative embodiment, the search may proceed from each rootcandidate in turn until any one of them returns a valid Presentationsegment. The first presentation segment found may be returned and thesearch may terminate. If no Presentation segment is found aftersearching through each root candidate, the longest Presentation segmentsearch process 2004 may report that no Presentation segment can be found(e.g., by outputting a partial segmentation record 2003B containing noPresentation segments).

According to some implementations, generating candidate Presentationsegments in the longest Presentation segment search process 2004 mayinvolve generating up to two new candidate Presentation segments in eachstep. In some examples, the first new candidate Presentation segment maybe generated by taking the existing candidate Presentation segment andmaking the end time later to include the next talkspurt uttered by thesame participant within a time interval being evaluated, which also maybe referred to herein as a “region of interest.” The second newcandidate Presentation segment may be generated by taking the existingcandidate Presentation segment and making the start time earlier toinclude the previous talkspurt uttered by the same participant withinthe region of interest. If there is no next or previous talkspurtuttered by the same participant within the region of interest, one orboth of the new candidate Presentation segments may not be generated. Analternative method of generating candidate Presentation segments will bedescribed below with reference to FIG. 23.

In some examples, the longest Presentation segment search process 2004may involve evaluating one or more acceptance criteria for new candidatePresentation segments. According to some such implementations, adominance metric may be calculated for each new candidate Presentationsegment. In some such implementations, the dominance metric may indicatea fraction of total speech uttered by a dominant conference participantduring a time interval that includes the new candidate Presentationsegment. The dominant conference participant may be the conferenceparticipant who spoke the most during the time interval. In someexamples, a new candidate Presentation segment having a dominance metricthat is greater than a dominance threshold will be added to the existingcandidate Presentation segment. In some implementations, the dominancethreshold may be 0.7, 0.75, 0.8, 0.85, etc. Otherwise, the search mayterminate.

In some implementations, a doubletalk ratio and/or a speech densitymetric may be evaluated during the Make Presentation process 2001, e.g.,during the longest Presentation segment search process 2004. Someexamples will be described below with reference to FIG. 22.

FIG. 21 outlines a subsequent stage of a segmentation process accordingto some implementations disclosed herein. In this example, a functioncall has been made to a subroutine that includes instructions for theMake Other process 2101.

The input speaker activity map 2102 to the Make Other process 2101 maydepend on the particular implementation. In some implementations, theinput speaker activity map 2102 may be the global speaker activity map1809, which indicates speaker activity for the entire conference, or aspeaker activity map corresponding to a smaller time interval. However,in some implementations the Make Other process 2101 may receive inputfrom one or more previous phases of the segmentation process, such asthe Make Babble process 1901 and/or the Make Presentation process 2001,indicating which time intervals of the conference (or which timeintervals of a portion or the conference) correspond topreviously-identified segments (such as previously-identified Babblesegments or Presentation segments). According to some suchimplementations, the input speaker activity map 2102 may correspond to atime interval that does not correspond to that of thepreviously-identified segments.

In this example, the Make Other process 2101 includes a longest segmentsearch process 2104, which may be capable of locating the longestsegment in the region of interest containing speech from one conferenceparticipant. Here, the Make Other process 2101 produces a partialsegmentation record 2103A containing one or more classified segments ora partial segmentation record 2103B containing a single classifiedsegment, depending on the results of the longest segment search process2104. In some examples, if the Make Other process 2101 produces apartial segmentation record 2103B it will be input to a classifier, suchas the classifier 2201 that is described below with reference to FIG.22. The Make Other process 2101 may involve an iterative process ofperforming the segment search process 2104 for each conferenceparticipant whose speech has been identified in the region of interest.

In this example, a root candidate segment may be generated substantiallyas described above with reference to the longest Presentation segmentsearch process 2004. For each root candidate talkspurt, someimplementations involve searching through the all of the talkspurts inthe region of interest uttered by the same conference participant as theroot candidate. Some examples involve building a candidate segment thatincludes of the longest run of such talkspurts containing the rootcandidate.

Some such examples involve applying one or more acceptance criteria. Insome implementations, one such criterion is that no two talkspurts maybe separated by more than a threshold candidate segment time intervalt_(window). An example setting for t_(window) is t_(min)/2, whereint_(min) represents the threshold candidate segment time (a minimum timeduration for a candidate segment). Other implementations may apply adifferent threshold candidate segment time interval and/or otheracceptance criteria. Some implementations may involve building acandidate segment by evaluating the next talkspurt by the sameconference participant and/or the previous talkspurt by the sameconference participant, e.g. as described above or as described belowwith reference to FIG. 23.

After the search is complete, the longest candidate segment (afteranalyzing all root candidates) may be classified. In this example, thelongest candidate segment is passed to the classifier 2201, whichreturns a classified longest segment 2106B. In the example shown in FIG.21, the preceding speaker activity map 2106A is input to the precedingspeaker activity recursion 2107A, which outputs the preceding partialsegmentation record 2108A. Here, the subsequent speaker activity map2106C is input to the subsequent speaker activity recursion 2107C, whichoutputs the subsequent partial segmentation record 1908C.

FIG. 22 outlines operations that may be performed by a segmentclassifier according to some implementations disclosed herein. In thisexample, given a speaker activity map 2202 for times t₀ to t₁ as input,the classifier 2201 is capable of determining an instance of one of thesegment classifications 2209A-2209E. In this example, the speakeractivity map 2202 includes a portion of the global speaker activity map1809 and is limited to contain information only in a temporal region ofinterest between times t₀ and t₁. In some implementations, theclassifier 2201 may be used in conjunction with one or more of therecursive segmentation processes described elsewhere herein. However, inalternative implementations, the classifier 2201 may be used in anon-recursive segmentation process. According to some suchimplementations, the classifier 2201 may be used to identify segments ineach of a plurality of time intervals (e.g., of sequential timeintervals) of a conference recording, or a part thereof.

In this implementation, the classifier 2201 includes a feature extractor2203, which is capable of analyzing conversational dynamics of thespeaker activity map 2202 and identifying conversational dynamics datatypes DT, DEN and DOM, which in this example correspond to a doubletalkratio, a speech density metric and a dominance metric, respectively.Here, the classifier 2201 is capable of determining instances of thesegment classifications according to a set of rules, which in thisexample are based on one or more of the conversational dynamics datatypes identified by the feature extractor 2203.

In this example, the set of rules includes a rule that classifies asegment as a Mutual Silence segment 2209A if the speech density metricDEN is less than a mutual silence threshold DEN_(s). Here, this rule isapplied by the Mutual Silence determination process 2204. In someimplementations, the mutual silence threshold DEN_(s) may be 0.1, 0.2,0.3, etc.

In this example, if the Mutual Silence determination process 2204determines that the speech density metric is greater than or equal tothe mutual silence threshold, the next process is the Babbledetermination process 2205. Here, the set of rules includes a rule thatclassifies a segment as a Babble segment if the speech density metric isgreater than or equal to the mutual silence threshold and the doubletalkratio DT is greater than a babble threshold DT_(B). In someimplementations, the babble threshold DT_(B) may be 0.6, 0.7, 0.8, etc.Accordingly, if the Babble determination process 2205 determines thatthe doubletalk ratio is greater than the babble threshold, the Babbledetermination process 2205 classifies the segment as a Babble segment2209B.

Here, if the Babble determination process 2205 determines that thedoubletalk ratio is less than or equal to the babble threshold, the nextprocess is the Discussion determination process 2206. Here, the set ofrules includes a rule that classifies a segment as a Discussion segmentif the speech density metric is greater than or equal to the silencethreshold and if the doubletalk ratio is less than or equal to thebabble threshold but greater than a discussion threshold DT_(D). In someimplementations, the discussion threshold DT_(D) may be 0.2, 0.3, 0.4,etc. Therefore, if the Discussion determination process 2206 determinesthat the doubletalk ratio is greater than the discussion thresholdDT_(D), classifies a segment as a Discussion segment 2209C.

In this implementation, if the Discussion determination process 2206determines that the doubletalk ratio is not greater than the discussionthreshold DT_(D), the next process is the Presentation determinationprocess 2207. Here, the set of rules includes a rule that classifies asegment as a Presentation segment if the speech density metric isgreater than or equal to the silence threshold, if the doubletalk ratiois less than or equal to the discussion threshold and if the dominancemetric DOM is greater than a presentation threshold DOM_(P). In someimplementations, the presentation threshold DOM_(P) may be 0.7, 0.8,0.9, etc. Accordingly, if the Presentation determination process 2207determines that the dominance metric DOM is greater than thepresentation threshold DOM_(P), the Presentation determination process2207 classifies the segment as a Presentation segment 2209D.

In this example, if the Presentation determination process 2207determines that the dominance metric DOM is not greater than apresentation threshold DOM_(P), the next process is the question andanswer determination process 2208. Here, the set of rules includes arule that classifies a segment as a Question and Answer segment if thespeech density metric is greater than or equal to the silence threshold,if the doubletalk ratio is less than or equal to the discussionthreshold and if the dominance metric is less than or equal to thepresentation threshold but greater than a question and answer threshold.

In some implementations, the question and answer threshold may be afunction of the number N of total conference participants, or ofconference participants whose speech has been identified in the regionof interest. According to some examples, the question and answerthreshold may be DOM_(Q)/N, wherein DOM_(Q) represents a constant. Insome examples, DOM_(Q) may equal 1.5, 2.0, 2.5, etc.

Therefore, if the question and answer determination process 2208determines that the dominance metric is greater than the question andanswer threshold, in this example the segment will be classified as aQ&A segment 2209E. If not, in this example the segment will beclassified as a Discussion segment 2209C.

FIG. 23 shows an example of a longest segment search process accordingto some implementations disclosed herein. According to someimplementations, such as those described above, the Make Babble, MakePresentation and Make Other processes each contain a correspondinglongest segment search process. In some such implementations, thelongest segment search process may proceed as follows. This example willinvolve a longest Presentation segment search process.

Here, a list of candidate seed talkbursts 2302A-2302F, included in aninput speaker activity map 2301, are evaluated. In some examples, ashere, the list of candidate seed talkbursts may be sorted in descendingorder of length, even though the list of candidate seed talkbursts isarranged in FIG. 23 according to start and end times. Next, each of thecandidate seed talkbursts may be considered in turn. In this example,the longest candidate seed talkburst (2302C) is considered first. Foreach candidate seed talkburst, a candidate segment may be designated.Here, the candidate segment 2304A is initially designated for candidateseed talkburst 2302C.

In this implementation, a first iteration 2303A involves classifying thecandidate segment 2304A (here, by the classifier 2201) to ensure thatits conversational dynamics data types (for example, the DEN, DT and/orDOM conversational dynamics data types described above) do not precludethe candidate segment 2304A from belonging to the particular segmentclassification being sought in the longest segment search process. Inthis example, the candidate segment 2304A includes only the candidatetalkburst 2302C, which is classified as a Presentation segment (2305A).Because this is the segment classification being sought in the longestsegment search process, the longest segment search process continues.

In this example, the second iteration 2303B of the longest segmentsearch process involves adding the following talkburst 2302D to thecandidate segment 2304A, to create the candidate segment 2304B, andclassifying the candidate segment 2304B. In some implementations,preceding and/or following talkbursts may need to be within a thresholdtime interval of the candidate segment in order to be eligible for beingadded to the candidate segment. If adding the following talkburstprecludes classification as the segment classification being sought, thefollowing talkburst may not be included in the candidate segment.However, in this example, the candidate segment 2304B is classified as aPresentation segment (2305B), so the candidate segment 2304B is kept anditeration continues.

In this implementation, the third iteration 2303C of the longest segmentsearch process involves adding the preceding talkburst 2302B to thecandidate segment 2304B, to create the candidate segment 2304C, andclassifying the candidate segment 2304C. In this example, the candidatesegment 2304C is classified as a Presentation segment (2305C), so thecandidate segment 2304C is kept and iteration continues.

In this example, the fourth iteration 2303D of the longest segmentsearch process involves adding the following talkburst 2302E to thecandidate segment 2304C, to create the candidate segment 2304D, andclassifying the candidate segment 2304D. In this example, the candidatesegment 2304D is classified as a Presentation segment (2305D) so thecandidate segment 2304D is kept and iteration continues.

Following and/or preceding talkbursts may continue to be added to thecandidate segment until adding either talkburst would mean that thecandidate segment is no longer of the sought class. Here, for example,the fifth iteration 2303E of the longest segment search process involvesadding the preceding talkburst 2302A to the candidate segment 2304D, tocreate the candidate segment 2304E, and classifying the candidatesegment 2304E. In this example, the candidate segment 2304E isclassified as a Q&A segment (2305E) so the candidate segment 2304E isnot kept.

However, in this example, the process continues in order to evaluate thefollowing talkburst. In the example shown in FIG. 23, the sixthiteration 2303F of the longest segment search process involves addingthe following talkburst 2302F to the candidate segment 2304D, to createthe candidate segment 2304E, and classifying the candidate segment2304F. In this example, the candidate segment 2304F is classified as aQ&A segment (2305E) so the candidate segment 2304C is not kept and theiterations cease.

If the resulting candidate segment is not shorter than a thresholdcandidate segment time t_(min), the candidate segment may be designatedas the longest segment. Otherwise, the longest segment search processmay report that no suitable segment exists. As noted elsewhere herein,the threshold candidate segment time t_(min) may vary according to thetimescale, which may correspond to the time interval of the region ofinterest. In this example, the candidate segment 2304D is longer thanthe threshold candidate segment time t_(min), so the longest segmentsearch process outputs the Presentation segment 2306.

Conference recordings typically include a large amount of audio data,which may include a substantial amount of babble and non-substantivediscussion. Locating relevant meeting topics via audio playback can bevery time-consuming. Automatic speech recognition (ASR) has sometimesbeen used to convert meeting recordings to text to enable text-basedsearch and browsing.

Unfortunately, accurate meeting transcription based on automatic speechrecognition has proven to be a challenging task. For example, theleading benchmark from the National Institute of Standards andTechnology (NIST) has shown that although the word error rate (WER) forASR of various types of speech has declined substantially in recentdecades, the WER for meeting speech has remained substantially higherthan the WER for other types of speech. According to a NIST reportpublished in 2007, the WER for meeting speech was typically more than25%, and frequently more than 50%, for meetings involving multipleconference participants. (Fiscus, Jonathan G., et al., “The RichTranscription 2007 Meeting Recognition Evaluation” (NIST 2007).)

Despite the known high WER for meeting speech, prior attempts togenerate meeting topics automatically were typically based on theassumption that ASR results of conference recordings produced a perfecttranscript of words spoken by conference participants. This disclosureincludes various novel techniques for determining meeting topics. Someimplementations involve word cloud generation, which may be interactiveduring playback. Some examples enable efficient topic mining whileaddressing the challenges provided by ASR errors.

According to some implementations, many hypotheses for a given utterance(e.g., as described in a speech recognition lattice) may contribute to aword cloud. In some examples, a whole-conference (or a multi-conference)context may be introduced by compiling lists of alternative hypothesesfor many words found in an entire conference and/or found in multipleconferences. Some implementations may involve applying awhole-conference (or a multi-conference) context over multipleiterations to re-score the hypothesized words of speech recognitionlattices (e.g., by de-emphasizing less-frequent alternatives), therebyremoving some utterance-level ambiguity.

In some examples, a “term frequency metric” may be used to sort primaryword candidates and alternative word hypotheses. In some such examples,the term frequency metric may be based, at least in part, on a number ofoccurrences of a hypothesized word in the speech recognition latticesand the word recognition confidence score reported by the speechrecognizer. In some examples, the term frequency metric may be based, atleast in part, on the frequency of a word in the underlying languageand/or the number of different meanings that a word may have. In someimplementations, words may be generalized into topics using an ontologythat may include hypernym information.

FIG. 24 is a flow diagram that outlines blocks of some topic analysismethods disclosed herein. The blocks of method 2400, like other methodsdescribed herein, are not necessarily performed in the order indicated.Moreover, such methods may include more or fewer blocks than shownand/or described.

In some implementations, method 2400 may be implemented, at least inpart, via instructions (e.g., software) stored on non-transitory mediasuch as those described herein, including but not limited to randomaccess memory (RAM) devices, read-only memory (ROM) devices, etc. Insome implementations, method 2400 may be implemented, at least in part,by an apparatus such as that shown in FIG. 3A. According to some suchimplementations, method 2400 may be implemented, at least in part, byone or more elements of the analysis engine 307 shown in FIGS. 3C and 5,e.g., by the joint analysis module 306. According to some such examples,method 2400 may be implemented, at least in part, by the topic analysismodule 525 of FIG. 5.

In this example, block 2405 involves receiving speech recognitionresults data for at least a portion of a conference recording of aconference involving a plurality of conference participants. In someexamples, speech recognition results data may be received by a topicanalysis module in block 2405. Here, the speech recognition results datainclude a plurality of speech recognition lattices and a wordrecognition confidence score for each of a plurality of hypothesizedwords of the speech recognition lattices. In this implementation, theword recognition confidence score corresponds with a likelihood of ahypothesized word correctly corresponding with an actual word spoken bya conference participant during the conference. In some implementations,speech recognition results data from two or more automatic speechrecognition processes may be received in block 2405. Some examples aredescribed below.

In some implementations, the conference recording may include conferenceparticipant speech data from multiple endpoints, recorded separately.Alternatively, or additionally the conference recording may includeconference participant speech data from a single endpoint correspondingto multiple conference participants and including information foridentifying conference participant speech for each conferenceparticipant of the multiple conference participants.

In the example shown in FIG. 24, block 2410 involves determining aprimary word candidate and one or more alternative word hypotheses foreach of a plurality of hypothesized words in the speech recognitionlattices. Here, the primary word candidate has a word recognitionconfidence score indicating a higher likelihood of correctlycorresponding with the actual word spoken by a conference participantduring the conference than a word recognition confidence score of any ofthe alternative word hypotheses.

In this implementation, block 2415 involves calculating a “termfrequency metric” for the primary word candidates and the alternativeword hypotheses. In this example, the term frequency metric is based, atleast in part, on a number of occurrences of a hypothesized word in thespeech recognition lattices and on the word recognition confidencescore.

According to some examples, the term frequency metric may be based, atleast in part, on a “document frequency metric.” In some such examples,the term frequency metric may be inversely proportional to the documentfrequency metric. The document frequency metric may, for example,correspond to an expected frequency with which a primary word candidatewill occur in the conference.

In some implementations, the document frequency metric may correspond toa frequency with which the primary word candidate has occurred in two ormore prior conferences. The prior conferences may, for example, beconferences in the same category, e.g., business conferences, medicalconferences, engineering conferences, legal conferences, etc. In someimplementations, conferences may be categorized by sub-category, e.g.,the category of engineering conferences may include sub-categories ofelectrical engineering conferences, mechanical engineering conferences,audio engineering conferences, materials science conferences, chemicalengineering conferences, etc. Likewise, the category of businessconferences may include sub-categories of sales conferences, financeconferences, marketing conferences, etc. In some examples, theconferences may be categorized, at least in part, according to theconference participants.

Alternatively, or additionally, the document frequency metric maycorrespond to a frequency with which the primary word candidate occursin at least one language model, which may estimate the relativelikelihood of different words and/or phrases, e.g., by assigning aprobability to a sequence of words according to a probabilitydistribution. The language model(s) may provide context to distinguishbetween words and phrases that sound similar. A language model may, forexample, be a statistical language model such as a unigram model, anN-gram model, a factored language model, etc. In some implementations, alanguage model may correspond with a conference type, e.g., with theexpected subject matter of a conference. For example, a language modelpertaining to medical terms may assign higher probabilities to the words“spleen” and “infarction” than a language model pertaining tonon-medical speech.

According to some implementations, conference category, conferencesub-category, and/or language model information may be received with thespeech recognition results data in block 2405. In some suchimplementations, such information may be included with the conferencemetadata 210 received by the topic analysis module 525 of FIG. 5.

Various alternative examples of determining term frequency metrics aredisclosed herein. In some implementations, the term frequency metric maybe based, at least in part, on a number of word meanings. In some suchimplementations, the term frequency metric may be based, at least inpart, on the number of definitions of the corresponding word in astandard reference, such as a particular lexicon or dictionary.

In the example shown in FIG. 24, block 2420 involves sorting the primaryword candidates and alternative word hypotheses according to the termfrequency metric. In some implementations, block 2420 may involvesorting the primary word candidates and alternative word hypotheses indescending order of the term frequency metric.

In this implementation block 2425 involves including the alternativeword hypotheses in an alternative hypothesis list. In someimplementations, iterations of at least some processes of method 2400may be based, at least in part, on the alternative hypothesis list.Accordingly, some implementations may involve retaining the alternativehypothesis list during one or more such iterations, e.g., after eachiteration.

In this example, block 2430 involves re-scoring at least somehypothesized words of the speech recognition lattices according to thealternative hypothesis list. In other words, a word recognitionconfidence score that is received for one or more hypothesized words ofthe speech recognition lattices in block 2405 may be changed during oneor more such iterations of the determining, calculating, sorting,including and/or re-scoring processes. Further details and examples areprovided below.

In some examples, method 2400 may involve forming a word list thatincludes primary word candidates and a term frequency metric for each ofthe primary word candidates. In some examples, the word list also mayinclude one or more alternative word hypotheses for each primary wordcandidate. The alternative word hypotheses may for example, be generatedaccording to a language model.

Some implementations may involve generating a topic list of conferencetopics based, at least in part, on the word list. The topic list mayinclude one or more words of the word list. Some such implementationsmay involve determining a topic score. For example, such implementationsmay determine whether to include a word on the topic last based, atleast in part, on the topic score. According to some implementations,the topic score may be based, at least in part, on the term frequencymetric.

In some examples, the topic score may be based, at least in part, on anontology for topic generalization. In linguistics, a hyponym is a wordor phrase whose semantic field is included within that of another word,known as its hypernym. A hyponym shares a “type-of” relationship withits hypernym. For example, “robin,” “starling,” “sparrow,” “crow” and“pigeon” are all hyponyms of “bird” (their hypernym); which, in turn, isa hyponym of “animal.”

Accordingly, in some implementations generating the topic list mayinvolve determining at least one hypernym of one or more words of theword list. Such implementations may involve determining a topic scorebased, at least in part on a hypernym score. In some implementations,the hypernyms need not have been spoken by a conference participant inorder to be part of the topic score determination process. Some examplesare provided below.

According to some implementations, multiple iterations of a least someprocesses of method 2400 may include iterations of generating the topiclist and determining the topic score. In some such implementations,block 2425 may involve including alternative word hypotheses in thealternative hypothesis list based, at least in part, on the topic score.Some implementations are described below, following some examples ofusing hypernyms as part of a process of determining a topic score.

In some examples, method 2400 may involve reducing at least somehypothesized words of a speech recognition lattice to a canonical baseform. In some such examples, the reducing process may involve reducingnouns of the speech recognition lattice to the canonical base form. Thecanonical base form may be a singular form of a noun. Alternatively, oradditionally, the reducing process may involve reducing verbs of thespeech recognition lattice to the canonical base form. The canonicalbase form may be an infinitive form of a verb.

FIG. 25 shows examples of topic analysis module elements. As with otherimplementations disclosed herein, other implementations of the topicanalysis module 525 may include more, fewer and/or other elements. Thetopic analysis module 525 may, for example, be implemented via a controlsystem, such as that shown in FIG. 3A. The control system may include atleast one of a general purpose single- or multi-chip processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic device, discrete gate or transistor logic, ordiscrete hardware components. In some implementations, the topicanalysis module 525 may be implemented via instructions (e.g., software)stored on non-transitory media such as those described herein, includingbut not limited to random access memory (RAM) devices, read-only memory(ROM) devices, etc.

In this example, the topic analysis module 525 is shown receiving speechrecognition lattices 2501. The speech recognition lattices 2501 may, forexample, be instance of speech recognition results such as the speechrecognition results 401F-405F that are described above with reference toFIGS. 4 and 5. Some examples of speech recognition lattices aredescribed below.

This example of the topic analysis module 525 includes a latticerescoring unit 2502. In some implementations, the lattice rescoring unit2502 may be capable of re-scoring at least some hypothesized words ofthe speech recognition lattices 2501 according to the alternativehypothesis list. For example, the lattice rescoring unit 2502 may becapable of changing the word recognition confidence score ofhypothesized words that are found in the alternative hypothesis list2507 such that these hypothesized words are de-emphasized. This processmay depend on the particular metric used for the word recognitionconfidence score. For example, in some implementations a wordrecognition confidence score may be expressed in terms of a cost, thevalues of which may be a measure of how unlikely a hypothesized word isto be correct. According to such implementations, de-emphasizing suchhypothesized words may involve increasing a corresponding wordrecognition confidence score.

According to some implementations, the alternative hypothesis list 2507may initially be empty. If so, the lattice rescoring unit 2502 mayperform no re-scoring until a later iteration.

In this example, the topic analysis module 525 includes a latticepruning unit 2503. The lattice pruning unit 2503 may, for example, becapable of performing one or more types of lattice pruning operations(such as beam pruning, posterior probability pruning and/or latticedepth limiting) in order to reduce the complexity of input the speechrecognition lattices 2501.

FIG. 26 shows an example of an input speech recognition lattice. Asshown in FIG. 26, un-pruned speech recognition lattices can be quitelarge. The circles in FIG. 26 represent nodes of the speech recognitionlattice. The curved lines or “arcs” connecting the nodes correspond withhypothesized words, which may be connected via the arcs to formhypothesized word sequences.

FIG. 27, which includes FIGS. 27A and 27B, shows an example of a portionof a small speech recognition lattice after pruning. In this example,the pruned speech recognition lattice corresponds to a first portion ofthe utterance “I accidentally did not finish my beef jerky coming fromSan Francisco to Australia.” In this example, alternative wordhypotheses for the same hypothesized word are indicated on arcs betweennumbered nodes. Different arcs of the speech recognition lattice may betraversed to form alternative hypothesized word sequences. For example,the hypothesized word sequence “didn't finish” is represented by arcsconnecting nodes 2, 6 and 8. The hypothesized word sequence “did offinish” is represented by arcs connecting nodes 5, 11, 12 and 15. Thehypothesized word sequence “did of finished” is represented by arcsconnecting nodes 5, 11, 12 and 14. The hypothesized word sequence “didnot finish” is represented by arcs connecting nodes 5, 11 and 17-20. Thehypothesized word sequence “did not finished” is represented by arcsconnecting nodes 5, 11, 17 and 18. All of the foregoing hypothesizedword sequences correspond to the actual sub-utterance “did not finish.”

In some speech recognition systems, the speech recognizer may report aword recognition confidence score in terms of a logarithmic acousticcost C_(A), which is a measure of how unlikely this hypothesized word onthis path through the lattice is to be correct, given the acoustic inputfeatures to the speech recognizer. The speech recognizer also may reporta word recognition confidence score in terms of a logarithmic languagecost C_(L), which is a measure of how unlikely this hypothesized word onthis path through the lattice is to be correct given the language model.The acoustic and language costs may be reported for each arc in thelattice.

For each arc in the lattice portion shown in FIG. 27, for example, thecombined acoustic and language cost (C_(A)+C_(L)) for that arc is shownnext to each hypothesized word. In this example, the best hypothesizedword sequence through the speech recognition lattice corresponds withthe path from the start node to an end node that has the lowest sum ofarc costs.

In the example shown in FIG. 25, the topic analysis module 525 includesa morphology unit 2504. The morphology unit 2504 may be capable ofreducing hypothesized words to a canonical base form. For example, insome implementations that involve reducing nouns of the speechrecognition lattice to the canonical base form, the morphology unit 2504may be capable of reducing plural forms of a noun to singular forms (forexample, reducing “cars” to “car”). In some implementations that involvereducing verbs of the speech recognition lattice to the canonical baseform, the morphology unit 2504 may be capable of reducing a verb to aninfinitive form (for example, reducing “running,” “ran,” or “runs” to“run”).

Alternative implementations of the morphology unit 2504 may include aso-called “stemmer,” such as a Porter Stemmer. However, a basic stemmerof this type may not be capable of accurately transforming irregularnoun or verb forms (such as reducing “mice” to “mouse”). A more accuratemorphology implementation may be needed for such transformations, suchas the WordNet morphology described in Miller, George A, WordNet: ALexical Database for English, in Communications of the ACM Vol. 38, No.11, pages 39-41 (1995).

The topic analysis module 525 of FIG. 25 includes a term frequencymetric calculator 2505. In some implementations, the term frequencymetric calculator 2505 may be capable of determining a term frequencymetric for hypothesized words of the speech recognition lattices 2501.In some such implementations, the term frequency metric calculator 2505may be capable of determining a term frequency metric for each nounobserved in the input lattices (for example, the morphology unit 2504may be capable of determining which hypothesized words are nouns).

In some implementations, the term frequency metric calculator 2505 maybe capable of determining a term frequency metric according to a TermFrequency/Inverse Document Frequency (TF-IDF) function. In one suchexample, each time a hypothesized word with index x of a lexicon isdetected in the input speech recognition lattices, the term frequencymetric TF_(x) may be determined as follows:

$\begin{matrix}{{TFx} = {{TFx}^{\prime} + \frac{c}{N \cdot {\max \left( {{\ln \; {DF}_{x}},{MDF}} \right)}}}} & \left( {{Equation}\mspace{14mu} 45} \right)\end{matrix}$

In Equation 45, TF_(x)′ represents the previous term frequency metricfor the word x. If this is the first time that the word x has beenencountered during the current iteration, the value of TF_(X)′ may beset to zero. In Equation 45, DF_(x) represents a document frequencymetric and ln indicates the natural logarithm. As noted above, thedocument frequency metric may correspond to an expected frequency withwhich a word will occur in the conference. In some examples, theexpected frequency may correspond to a frequency with which the word hasoccurred in two or more prior conferences. In the case of a generalbusiness teleconference system, the document frequency metric may bederived by counting the frequency with which this word appears across alarge number of business teleconferences.

Alternatively, or additionally, the expected frequency may correspond toa frequency with which the primary word candidate occurs in a languagemodel. Various implementations of methods disclosed herein may be usedin conjunction with a speech recognizer, which may apply some type ofword frequency metric as part of its language model. Accordingly, insome implementations a language model used for speech recognition mayprovide the document frequency metric used by the term frequency metriccalculator 2505. In some implementations, such information may beprovided along with the speech recognition lattices or included with theconference metadata 210.

In Equation 45, MDF represents a selected constant that indicates aminimum logarithmic document frequency. In some implementations, MDFvalues may be integers in the range of −10 to −4, e.g., −6.

In Equation 45, C represents a word recognition confidence score in therange [0-1] as reported by the speech recognizer in the input lattice.According to some implementations, C may be determined according to:

C=exp(−C _(A) −C _(L))  (Equation 46)

In Equation 46, C_(A) represents logarithmic acoustic cost and C_(L)represents the logarithmic language cost, both of which are representedusing the natural logarithm.

In Equation 45, N represents a number of word meanings. In someimplementations, the value of N may be based on the number ofdefinitions of the word in a standard lexicon, such as that of aparticular dictionary.

According to some alternative implementations, the term frequency metricTF_(x) may be determined as follows:

$\begin{matrix}{{TFx} = {{TFx}^{\prime} + \frac{{\alpha \; C} + \left( {1 - \alpha} \right)}{N \cdot {\max \left( {{\ln \; {DF}_{x}},{MDF}} \right)}}}} & \left( {{Equation}\mspace{14mu} 47} \right)\end{matrix}$

In Equation 47, α represents a weight factor that may, for example, havea value in the range of zero to one. In Equation 45, the recognitionconfidence C is used in an un-weighted manner. In some instances, anun-weighted recognition confidence C could be non-optimal, e.g., if ahypothesized word has a very high recognition confidence but appearsless frequently. Therefore, adding the weight factor α may help tocontrol the importance of recognition confidence. It may be seen thatwhen α=1, the Equation 47 is equivalent to Equation 45. However, whenα=0, recognition confidence is not used and the term frequency metricmay be determined according the inverse of the terms in the denominator.

In the example shown in FIG. 25, the topic analysis module 525 includesan alternative word hypothesis pruning unit 2506. As the word list 2508is created, the system notes a set of alternative word hypotheses foreach word by analyzing alternative paths through the lattice for thesame time interval.

For example, if the actual word spoken by a conference participant wasthe word pet, the speech recognizer may have reported put and pat asalternative word hypotheses. For a second instance of the actual wordpet, the speech recognizer may have reported pat, pebble and parent asalternative word hypotheses. In this example, after analyzing all thespeech recognition lattices corresponding to all the utterances in theconference, the complete list of alternative word hypotheses for theword pet may include put, pat, pebble and parent. The word list 2508 maybe sorted in descending order of TF_(x).

In some implementations of the alternative word hypothesis pruning unit2506, alternative word hypotheses appearing further down the list (forexample, having a lower value of TF_(x)) may be removed from the list.Removed alternatives may be added to the alternative word hypothesislist 2507. For example, if the hypothesized word pet has a higher TF_(x)than its alternative word hypotheses, the alternative word hypothesispruning unit 2506 may remove the alternative word hypotheses pat, put,pebble and parent from the word list 2508 and add the alternative wordhypotheses pat, put, pebble and parent to the alternative wordhypothesis list 2507.

In this example, the topic analysis module 525 stores an alternativeword hypothesis list 2507 in memory, at least temporarily. Thealternative word hypothesis list 2507 may be input to the latticerescoring unit 2502, as described elsewhere, over a number ofiterations. The number of iterations may vary according to theparticular implementation and may be, for example, in the range 1 to 20.In one particular implementation, 4 iterations produced satisfactoryresults.

In some implementations, the word list 2508 may be deleted at the startof each iteration and may be re-compiled during the next iteration.According to some implementations, the alternative word hypothesis list2507 may not be deleted at the start of each iteration, so thealternative word hypothesis list 2507 may grow in size as the iterationscontinue.

In the example shown in FIG. 25, the topic analysis module 525 includesa topic scoring unit 2509. The topic scoring unit 2509 may be capable ofdetermining a topic score for words in the word list 2508.

In some examples, the topic score may be based, at least in part, on anontology 2510 for topic generalization, such as the WordNet ontologydiscussed elsewhere herein. Accordingly, in some implementationsgenerating the topic list may involve determining at least one hypernymof one or more words of the word list 2508. Such implementations mayinvolve determining a topic score based, at least in part, on a hypernymscore. In some implementations, the hypernyms need not have been spokenby a conference participant in order to be part of the topic scoredetermination process.

For example, a pet is an example of an animal, which is a type oforganism, which is a type of living thing. Therefore, the word “animal”may be considered a first-level hypernym of the word “pet.” The word“organism” may be considered a second-level hypernym of the word “pet”and a first-level hypernym of the word “animal.” The phrase “livingthing” may be considered a third-level hypernym of the word “pet,” asecond-level hypernym of the word “animal” and a first-level hypernym ofthe word “organism.”

Therefore, if the word “pet” is on the word list 2508, in someimplementations the topic scoring unit 2509 may be capable ofdetermining a topic score according to one of more of the hypernyms“animal,” “organism” and/or “living thing.” According to one suchexample, for each word on the word list 2508, the topic scoring unit2509 may traverse up the hypernym tree N levels (here, for example,N=2), adding each hypernym to the topic list 2511 if not already presentand adding the term frequency metric of the word to the topic scoreassociated with the hypernym. For example, if pet is present on the wordlist 2508 with a term frequency metric of 5, then pet, animal andorganism will be added to the topic list with a term frequency metric of5. If animal is also on the word list 2508 with term frequency metric of3, then the topic score of animal and organism will have 3 added for atotal topic score of 8, and living thing will be added to the word list2508 with a term frequency metric of 3.

According to some implementations, multiple iterations of a least someprocesses of method 2400 may include iterations of generating the topiclist and determining the topic score. In some such implementations,block 2525 of method 2400 may involve including alternative wordhypotheses in the alternative hypothesis list based, at least in part,on the topic score. For example, in some alternative implementations,the topic analysis module 525 may be capable of topic scoring based onthe output of the term frequency metric calculator 2505. According tosome such implementations, the alternative word hypothesis pruning unit2506 may perform alternative hypothesis pruning of topics, in additionto alternative word hypotheses.

For example, suppose that the topic analysis module 525 had determined aconference topic of “pets” due to a term frequency metric of 15 for oneor more instances of “pet,” a term frequency metric of 5 for an instanceof “dog” a term frequency metric of 4 for an instance of “goldfish.”Suppose further that there may be a single utterance of “cat” somewherein the conference, but there is significant ambiguity as to whether theis actual word spoken was “cat,” “mat,” “hat,” “catamaran,” “catenary,”“caterpillar,” etc. If the topic analysis module 525 had only beenconsidering word frequencies in the feedback loop, then the word list2508 would not facilitate a process of disambiguating these hypotheses,because there was only one potential utterance of “cat.” However,because “cat” is a hyponym of “pet,” which was identified as a topic byvirtue of other words spoken, then the topic analysis module 525 maypotentially be better able to disambiguate that potential utterance of“cat.”

In this example, the topic analysis module 525 includes a metadataprocessing unit 2515. According to some implementations, the metadataprocessing unit 2515 may be capable of producing a bias word list 2512that is based, at least in part, on the conference metadata 210 receivedby the topic analysis module 525. The bias word list 2512 may, forexample, be capable of including a list of words that may be inserteddirectly into the word list 2508 with a fixed term frequency metric. Themetadata processing unit 2515 may, for example, derive the bias wordlist 2512 from a priori information pertaining to the topic or subjectof the meeting, e.g., from a calendar invitation, from email, etc. Abias word list 2512 may bias a topic list building process to be morelikely to contain topics pertaining to a known subject of the meeting.

In some implementations, the alternative word hypotheses may begenerated according to multiple language models. For example, if theconference metadata were to indicate that a conference may involve legaland medical issues, such as medical malpractice issues corresponding toa lawsuit based on a patient's injury or death due to a medicalprocedure, the alternative word hypotheses may be generated according toboth medical and legal language models.

According to some such implementations, multiple language models may beinterpolated internally by an ASR process, so that the speechrecognition results data received in block 2405 of method 2400 and/orthe speech recognition lattices 2501 received in FIG. 25 are based onmultiple language models. In alternative implementations, the ASRprocess may output multiple sets of speech recognition lattices, eachset corresponding to a different language model. A topic list 2511 maybe generated for each type of input speech recognition lattice. Multipletopic lists 2511 may be may be merged into a single topic list 2511according to the resulting topic scores.

According to some implementations disclosed herein, the topic list 2511may be used to facilitate a process of playing back a conferencerecording, searching for topics in a conference recording, etc.According to some such implementations, the topic list 2511 may be usedto provide a “word cloud” of topics corresponding to some or all of theconference recording.

FIG. 28, which includes FIGS. 28A and 28B, shows an example of a userinterface that includes a word cloud for an entire conference recording.The user interface 606 a may be provided on a display and may be usedfor browsing the conference recording. For example, the user interface606 a may be provided on a display of a display device 610, as describedabove with reference to FIG. 6.

In this example, the user interface 606 a includes a list 2801 ofconference participants of the conference recording. Here, the userinterface 606 a shows waveforms 625 in time intervals corresponding toconference participant speech.

In this implementation, the user interface 606 a provides a word cloud2802 for an entire conference recording. Topics from the topic list 2511may be arranged in the word cloud 2802 in descending order of topicfrequency (e.g., from right to left) until no further room is available,e.g., given a minimum font size.

According to some such implementations, a topic placement algorithm forthe word cloud 2802 may be re-run each time the user adjusts a zoomratio. For example, a user may be able to interact with the userinterface 606 a (e.g., via touch, gesture, voice command, etc.) in orderto “zoom in” or enlarge at least a portion of the graphical userinterface 606, to show a smaller time interval than that of the entireconference recording. According to some such examples, the playbackcontrol module 605 of FIG. 6 may access a different instance of theconversational dynamics data files 515 a-515 n, which may have beenpreviously output by the conversational dynamics analysis module 510,that more closely corresponds with a user-selected time interval.

FIG. 29, which includes FIGS. 29A and 29B, shows an example of a userinterface that includes a word cloud for each of a plurality ofconference segments. As in the previous example, the user interface 606b includes a list 2801 of conference participants and shows waveforms625 in time intervals corresponding to conference participant speech.

However, in this implementation, the user interface 606 b provides aword cloud for each of a plurality of conference segments 1808A-1808J.According to some such implementations, the conference segments1808A-1808J may have previously been determined by a segmentation unit,such as the segmentation unit 1804 that is described above withreference to FIG. 18B. In some implementations, the topic analysismodule 525 may be invoked separately for each segment 1808 of theconference (for example, by using only the speech recognition lattices2501 corresponding to utterances from one segment 1808 at a time) togenerate a separate topic list 2511 for each segment 1808.

In some implementations, the size of the text used to render each topicin a word cloud may be made proportional to the topic frequency. In theimplementation shown in FIG. 29A, for example, the topics “kitten” and“newborn” are shown in a slightly larger font size than the topic “largeinteger,” indicating that the topics “kitten” and “newborn” werediscussed more than the topic “large integer” in the segment 1808C.However, in some implementations the text size of a topic may beconstrained by the area available for displaying a word cloud, a minimumfont size (which may be user-selectable), etc.

FIG. 30 is a flow diagram that outlines blocks of some playback controlmethods disclosed herein. The blocks of method 3000, like other methodsdescribed herein, are not necessarily performed in the order indicated.Moreover, such methods may include more or fewer blocks than shownand/or described.

In some implementations, method 3000 may be implemented, at least inpart, via instructions (e.g., software) stored on non-transitory mediasuch as those described herein, including but not limited to randomaccess memory (RAM) devices, read-only memory (ROM) devices, etc. Insome implementations, method 3000 may be implemented, at least in part,by an apparatus such as that shown in FIG. 3A. According to some suchimplementations, method 3000 may be implemented, at least in part, byone or more elements of the playback system 609 shown in FIG. 6, e.g.,by the playback control module 605.

In this example, block 3005 involves receiving a conference recording ofat least a portion of a conference involving a plurality of conferenceparticipants and a topic list of conference topics. In someimplementations, as shown in FIG. 6, block 3005 may involve receipt bythe playback system 609 of individual playback streams, such as theplayback streams 401B-403B. According to some such implementations,block 3005 may involve receiving other data, such as the playback streamindices 401A-403A, the analysis results 301C-303C, the segment and wordcloud data 309, the search index 310 and/or the meeting overviewinformation 311 received by the playback system 609 of FIG. 6.Accordingly, in some examples block 3005 may involve receivingconference segment data including conference segment time interval dataand conference segment classifications.

According to some implementations, block 3005 may involve receiving theconference recording and/or other information via an interface system.The interface system may include a network interface, an interfacebetween a control system and a memory system, an interface between thecontrol system and another device and/or an external device interface.

Here, block 3010 involves providing instructions for controlling adisplay to make a presentation of displayed conference topics for atleast a portion of the conference. In this example, the presentationincludes images of words corresponding to at least some of theconference topics, such as the word cloud 2802 shown in FIG. 28. In someimplementations, the playback control module 605 may provide suchinstructions for controlling a display in block 3010. For example, block3010 may involve providing such instructions to a display device, suchas the display device 610, via the interface system.

The display device 610 may, for example, be a laptop computer, a tabletcomputer, a smart phone or another type of device that is capable ofproviding a graphical user interface that includes a word cloud ofdisplayed conference topics, such as the graphical user interface 606 aof FIG. 28 or the graphical user interface 606 b of FIG. 29, on adisplay. For example, the display device 610 may be capable of executinga software application or “app” for providing the graphical userinterface according to instructions from the playback control module605, receiving user input, sending information to the playback controlmodule 605 corresponding to received user input, etc.

In some instances, the user input received by the playback controlmodule 605 may include an indication of a selected conference recordingtime interval chosen by a user, e.g., according to user inputcorresponding to a “zoom in” or a “zoom out” command. In response tosuch user input, the playback control module 605 may provide, via theinterface system, instructions for controlling the display to make thepresentation of displayed conference topics correspond with the selectedconference recording time interval. For example, the playback controlmodule 605 may select a different instance of a conversational dynamicsdata file (such as one of the conversational dynamics data files 515a-515 e that are shown to be output by the conversational dynamicsanalysis module 510 in FIG. 5) that most closely corresponds to theselected conference recording time interval chosen by the user andprovide corresponding instructions to the display device 610.

If block 3005 involves receiving conference segment data, the displaydevice 610 may be capable of controlling the display to presentindications of one or more conference segments and to make thepresentation of displayed conference topics indicate conference topicsdiscussed in the one or more conference segments, e.g., as shown in FIG.29. The display device 610 may be capable of controlling the display topresent waveforms corresponding to instances of conference participantspeech and/or images corresponding to conference participants, such asthose shown in FIGS. 28 and 29.

In the example shown in FIG. 30, block 3015 involves receiving anindication of a selected topic chosen by a user from among the displayedconference topics. In some examples, block 3015 may involve receiving,by the playback control module 605 and via the interface system, userinput from the display device 610. The user input may have been receivedvia user interaction with a portion of the display corresponding to theselected topic, e.g., an indication from a touch sensor system of auser's touch in an area of a displayed word cloud corresponding to theselected topic. Another example is shown in FIG. 31 and described below.In some implementations, if a user causes a cursor to hover over aparticular word in a displayed word cloud, instances of conferenceparticipant speech associated with that word may be played back. In someimplementations, the conference participant speech may be spatiallyrendered and/or played back in an overlapped fashion.

In the example shown in FIG. 30, block 3020 involves selecting playbackaudio data comprising one or more instances of speech of the conferencerecording that include the selected topic. For example, block 3020 mayinvolve selecting instances of speech corresponding to the selectedtopic, as well as at least some words spoken before and/or after theselected topic, in order to provide context. In some such examples,block 3020 may involve selecting utterances that include the selectedtopic.

In some implementations, block 3020 may involve selecting at least twoinstances of speech, including at least one instance of speech utteredby each of at least two conference participants. The method may involverendering the instances of speech to at least two different virtualconference participant positions of a virtual acoustic space to producerendered playback audio data, or accessing portions ofpreviously-rendered speech that include the selected topic. According tosome implementations, the method may involve scheduling at least aportion of the instances of speech for simultaneous playback.

According to some implementations, block 3015 may involve receiving anindication of a selected conference participant chosen by a user fromamong the plurality of conference participants. One such example isshown in FIG. 32 and described below. In some such implementations,block 3020 may involve selecting playback audio data that includes oneor more instances of speech of the conference recording that includespeech by the selected conference participant regarding the selectedtopic.

Here, block 3025 involves providing the playback audio data for playbackon a speaker system. For example, the playback system 609 may providemixed and rendered playback audio data, via the interface system, to thedisplay device 610 in block 3025. Alternatively, the playback system 609may provide the playback audio data directly to a speaker system, suchas the headphones 607 and/or the speaker array 608, in block 3025.

FIG. 31 shows an example of selecting a topic from a word cloud. In someimplementations, a display device 610 may provide the graphical userinterface 606 c on a display. In this example, a user has selected theword “pet” from the word cloud 2802 and has dragged a representation ofthe word to the search window 3105. In response, the display device maysend an indication of the selected topic “pet” to the playback controlmodule 605. Accordingly, this is an example of the “indication of aselected topic” that may be received in block 3015 of FIG. 30. Inresponse, the display device 610 may receive playback audio datacorresponding to one or more instances of speech that involve the topicof pets.

FIG. 32 shows an example of selecting both a topic from a word cloud anda conference participant from a list of conference participants. Asnoted above, a display device 610 may be providing the graphical userinterface 606 c on a display. In this example, after the user hasselected the word “pet” from the word cloud 2802, the user has dragged arepresentation of the conference participant George Washington to thesearch window 3105. The display device 610 may send an indication of theselected topic “pet” and the conference participant George Washington tothe playback control module 605. In response, the playback system 609may send the display device 610 playback audio data corresponding to oneor more instances of speech by the conference participant GeorgeWashington regarding the topic of pets.

When reviewing large numbers of teleconference recordings, or even asingle recording of a long teleconference, it can be time-consuming tomanually locate a part of a teleconference that one remembers. Somesystems have been previously described by which a user may search forkeywords in a speech recording by entering the text of a keyword that heor she wishes to locate. These keywords may be used for a search of textproduced by a speech recognition system. A list of results may bepresented to the user on a display screen.

Some implementations disclosed herein provide methods for presentingconference search results that may involve playing excerpts of theconference recording to the user very quickly, but in a way which isdesigned to allow the listener to attend to those results which interesthim or her. Some such implementations may be tailored for memoryaugmentation. For example, some such implementations may allow a user tosearch for one or more features of a conference (or multipleconferences) that the user remembers. Some implementations may allow auser to review the search results very quickly to find one or moreparticular instances that the user is looking for.

Some such examples involve spatial rendering techniques, such asrendering the conference participant speech data for each of theconference participants to a separate virtual conference participantposition. As described in detail elsewhere herein, some such techniquesmay allow the listener to hear a large amount of content quickly andthen select portions of interest for more detailed and/or slowerplayback. Some implementations may involve introducing or changingoverlap between instances of conference participant speech, e.g.,according to a set of perceptually-motivated rules. Alternatively, oradditionally, some implementations may involve speeding up theplayed-back conference participant speech. Accordingly, suchimplementations can make use of the human talent of selecting attentionto ensure that a desired search term is found, while minimizing the timethat the search process takes.

Accordingly, instead of returning a few results which are very likely tobe relevant to the user's search terms and asking the user toindividually audition each result (for example, by clicking on eachresult in a list, in turn, to play it), some such implementations mayreturn many search results that the user can audition quickly (forexample, in a few seconds) using spatial rendering and other fastplayback techniques disclosed herein. Some implementations may provide auser interface that allows the user to further explore (for example,audition at 1:1 playback speed) selected instances of the searchresults.

However, some examples disclosed herein may or may not involve spatialrendering, introducing or changing overlap between instances ofconference participant speech or speeding up the played-back conferenceparticipant speech, depending on the particular implementation.Moreover, some disclosed implementations may involve searching otherfeatures of one or more conferences in addition to, or instead of, thecontent. For example, in addition to searching for particular words inone or more teleconferences, some implementations may involve performinga concurrent search for multiple features of a conference recording. Insome examples, the features may include the emotional state of thespeaker, the identity of the speaker, the type of conversationaldynamics occurring at the time of an utterance (e.g. a presentation, adiscussion, a question and answer session, etc.), an endpoint location,an endpoint type and/or other features.

A concurrent search involving multiple features (which may sometimes bereferred to herein as a multi-dimensional search) can increase searchaccuracy and efficiency. For example, if a user could only perform akeyword search, e.g., for the word “sales” in a conference, the usermight have to listen to many results before finding a particular excerptof interest that the user may remember from the conference. In contrast,if the user were to perform a multi-dimensional search for instances ofthe word “sales” spoken by the conference participant Fred Jones, theuser could have potentially reduced the number results that the userwould need to review before finding an excerpt of interest.

Accordingly, some disclosed implementations provide methods and devicesfor efficiently specifying multi-dimensional search terms for one ormore teleconference recordings and for efficiently reviewing the searchresults to locate particular excerpts of interest.

FIG. 33 is a flow diagram that outlines blocks of some topic analysismethods disclosed herein. The blocks of method 3300, like other methodsdescribed herein, are not necessarily performed in the order indicated.Moreover, such methods may include more or fewer blocks than shownand/or described.

In some implementations, method 3300 may be implemented, at least inpart, via instructions (e.g., software) stored on non-transitory mediasuch as those described herein, including but not limited to randomaccess memory (RAM) devices, read-only memory (ROM) devices, etc. Insome implementations, method 3300 may be implemented, at least in part,by a control system, e.g., by a control system of an apparatus such asthat shown in FIG. 3A. The control system may include at least one of ageneral purpose single- or multi-chip processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, or discrete hardware components.According to some such implementations, method 3300 may be implemented,at least in part, by one or more elements of the playback system 609shown in FIG. 6, e.g., by the playback control module 605.

In this example, block 3305 involves receiving audio data correspondingto a recording of at least one conference involving a plurality ofconference participants. In this example, the audio data includesconference participant speech data from multiple endpoints, recordedseparately and/or conference participant speech data from a singleendpoint corresponding to multiple conference participants and includingspatial information for each conference participant of the multipleconference participants.

In the example shown in FIG. 33, block 3310 involves determining searchresults of a search of the audio data based on one or more searchparameters. According to some examples, determining the search resultsmay involve receiving search results. For example, in someimplementations one or more elements of a playback system, such as theplayback system 609 shown in FIG. 6, may perform some processes ofmethod 3300 and another device, such as a server, may perform otherprocesses of method 3300. According to some such implementations, theplayback control server 650 may perform a search and may provide thesearch results to the playback system 609, e.g., to the playback controlmodule 605.

In other examples, determining the search results in block 3310 mayinvolve actually performing a search. For example, in some suchimplementations the playback system 609 may be capable of performing asearch. As described in more detail below, the playback system 609and/or another device may be capable of performing the search accordingto user input, which may in some examples be received via a graphicaluser interface provided on a display device.

In some implementations, block 3310 may involve performing a concurrentsearch for multiple features of the audio data received in block 3305.Being able to perform a concurrent search for multiple features of theaudio data can provide many potential advantages, in part becauseconference participants will often remember many different aspects of aparticular meeting experience. One example described above involves amulti-dimensional search for instances of the word “sales” spoken by theconference participant Fred Jones. In a more detailed example, aconference participant may remember that Fred Jones was speaking about“sales” while giving a presentation sometime during a three-week timeinterval. The conference participant may have been able to determinefrom the tone of Fred Jones' voice that he was excited about the topic.The conference participant may remember that Fred Jones was talking on aheadset from his office in San Francisco. Each of these individualsearch features may not be very specific when used by itself, but whencombined together they may be very specific and could provide a veryfocused search.

In some examples, the features may include words, which may bedetermined according to a keyword spotting index from a speechrecognition program's internal speech recognition lattice structures,some examples of which are described in detail below. Suchimplementations may allow very fast searching of many of the concurrenthypotheses that a speech recognizer provided regarding which words wereuttered in the conference. Alternatively, or additionally, the wordsused in a search may correspond to conference topics determined from thespeech recognition lattices, e.g. by using the “word cloud” methodsdescribed above.

Various methods are disclosed herein of determining conference segments,which may be based on conversational dynamics. In some implementations,a multi-dimensional search may be based, at least in part, on searchingone or more types of conference segments.

In some implementations, a multi-dimensional search may be based, atleast in part, on conference participant identity. For a single-partyendpoint such as a mobile phone or a PC-based soft client, someimplementations may involve recording the name of each conferenceparticipant from the device ID. For Voice over Internet Protocol (VoIP)soft-client systems, a user is often prompted to enter his or her nameto enter the conference. The names may be recorded for future reference.For speakerphone devices it may be possible to use voiceprint analysisto identify each speaker around the device from among those peopleinvited to the meeting (if the list of invitees is known by therecording/analysis system, e.g., based on a meeting invitation). Someimplementations may allow a search based on a general classificationregarding conference participant identity, e.g., based on the fact thata conference participant is a male speaker of U.S. English.

In some examples, time may be a searchable feature. For example, ifconference recordings are stored along with their start and end timesand dates, some implementations may allow a user to search multipleconference recordings within a specified range of dates and/or times.

Some implementations may allow a user to search one or more conferencerecordings based on conference participant emotion. For example, theanalysis engine 307 may have performed one of more types of analyses onthe audio data to determine conference participant mood features (See,e.g., Bachorowski, J.-A., & Owren, M. J. (2007). Vocal expressions ofemotion. Lewis, M., Haviland-Jones, J. M., & Barrett, L. F. (Eds.), Thehandbook of emotion, 3rd Edition. New York: Guilford. (in press), whichis hereby incorporated by reference) such as excitement, aggression orstress/cognitive load from an audio recording. (See, e.g., Yap, TetFei., Speech production under cognitive load: Effects andclassification, Dissertation, The University of New South Wales (2012),which is hereby incorporated by reference.) In some implementations, theresults may be indexed, provided to the playback system 609 and used aspart of a multi-dimensional search.

In some examples, endpoint location may be a searchable feature. Forexample, for endpoints that are installed in a particular room, thelocation may be known a priori. Some implementations may involve logginga mobile endpoint location based on location information provided by anonboard GPS receiver. In some examples, a location of a VoIP client maybe located based on the endpoint's IP address.

Some implementations may allow a user to search one or more conferencerecordings based on endpoint type. If the meeting recording notesinformation about the type of telephony device used by each participant(e.g., the make and/or model of a telephone, the User Agent string for aweb-based soft client, the class of a device (headset, handset orspeakerphone), etc.), in some implementations this information may bestored as conference metadata, provided to the playback system 609 andused as part of a multi-dimensional search.

In some examples, block 3310 may involve performing a search of audiodata that corresponds to recordings of multiple conferences. Someexamples are described below.

In this example, the search results determined in block 3310 correspondto at least two instances of conference participant speech in the audiodata. Here, the at least two instances of conference participant speechinclude at least a first instance of speech uttered by a firstconference participant and at least a second instance of speech utteredby a second conference participant.

In this implementation, block 3315 involves rendering the instances ofconference participant speech to at least two different virtualconference participant positions of a virtual acoustic space, such thatthe first instance of speech is rendered to a first virtual conferenceparticipant position and the second instance of speech is rendered to asecond virtual conference participant position.

According to some such implementations, one or more elements of aplayback system, such as the mixing and rendering module 604 of theplayback system 609, may perform the rendering operations of block 3315.However, in some implementations the rendering operations of block 3315may be performed, at least in part, by another device, such as therendering server 660 shown in FIG. 6.

In some examples, whether the playback system 609 or another device(such as the rendering server 660) performs the rendering operations ofblock 3315 may depend, at least in part, on the complexity of therendering process. If, for example, the rendering operations of block3315 involve selecting a virtual conference participant position from aset of predetermined virtual conference participant positions, block3315 may not involve a large amount of computational overhead. Accordingto some such implementations, block 3315 may be performed by theplayback system 609.

However, in some implementations the rendering operations may be morecomplex. For example, some implementations may involve analyzing theaudio data to determine conversational dynamics data. The conversationaldynamics data may include data indicating the frequency and duration ofconference participant speech, data indicating instances of conferenceparticipant doubletalk (during which at least two conferenceparticipants are speaking simultaneously) and/or data indicatinginstances of conference participant conversations.

Some such examples may involve applying the conversational dynamics dataas one or more variables of a spatial optimization cost function of avector describing the virtual conference participant position for eachof the conference participants in the virtual acoustic space. Suchimplementations may involve applying an optimization technique to thespatial optimization cost function to determine a locally optimalsolution and assigning the virtual conference participant positions inthe virtual acoustic space based, at least in part, on the locallyoptimal solution.

In some such implementations, determining the conversational dynamicsdata, applying the optimization technique to the spatial optimizationcost function, etc., may be performed by a module other than theplayback system 609, e.g., by the playback control server 650. In someimplementations, at least some of these operations may have previouslybeen performed, e.g., by the playback control server 650 or by the jointanalysis module 306. According to some such implementations, block 3315may involve receiving the output of such a process, e.g., receiving, bythe mixing and rendering module 604, assigned virtual conferenceparticipant positions and rendering the instances of conferenceparticipant speech to at least two different virtual conferenceparticipant positions.

In the example shown in FIG. 33, block 3320 involves scheduling at leasta portion of the instances of conference participant speech forsimultaneous playback, to produce playback audio data. In someimplementations, the scheduling may involve scheduling the instances ofconference participant speech for playback based, at least in part, on asearch relevance metric. For example, instead of scheduling conferenceparticipant speech for playback according to, e.g., the start time ofeach of the instances of conference participant speech, some suchimplementations may involve scheduling conference participant speechhaving a relatively higher search relevance metric for playback earlierthan conference participant speech having a relatively lower searchrelevance metric. Some examples are described below.

According to some implementations, block 3320 may involve scheduling aninstance of conference participant speech that did not previouslyoverlap in time to be played back overlapped in time and/or schedulingan instance of conference participant speech that was previouslyoverlapped in time to be played back further overlapped in time. In someinstances, such scheduling may be performed according to a set ofperceptually-motivated rules, e.g., as disclosed elsewhere herein.

For example, the set of perceptually-motivated rules may include a ruleindicating that two talkspurts of a single conference participant shouldnot overlap in time and/or a rule indicating that two talkspurts shouldnot overlap in time if the two talkspurts correspond to a singleendpoint. In some implementations, the set of perceptually-motivatedrules may include a rule wherein, given two consecutive input talkspurtsA and B, A having occurred before B, the playback of an output talkspurtcorresponding to B may begin before the playback of an output talkspurtcorresponding to A is complete, but not before the playback of theoutput talkspurt corresponding to A has started. In some examples, theset of perceptually-motivated rules may include a rule allowing theplayback of an output talkspurt corresponding to B to begin no soonerthan a time T before the playback of an output talkspurt correspondingto A is complete, wherein T is greater than zero.

According to some implementations, method 3300 may involve providing theplayback audio data to a speaker system. Alternatively, or additionally,method 3300 may involve providing the playback audio data to anotherdevice, such as the display device 610 of FIG. 6, which may be capableof providing the playback audio data to a speaker system (e.g., theheadphones 607, ear buds, the speaker array 608, etc.).

FIG. 34 is a block diagram that shows examples of search systemelements. In this implementation, the search system 3420 includes asearch module 3421, an expansion unit 3425, a merging unit 3426 and aplayback scheduling unit 3406. In some implementations, the searchmodule 3421, the expansion unit 3425, the merging unit 3426 and/or theplayback scheduling unit 3406 may be implemented, at least in part, viainstructions (e.g., software) stored on non-transitory media such asthose described herein, including but not limited to random accessmemory (RAM) devices, read-only memory (ROM) devices, etc. In someimplementations, the search module 3421, the expansion unit 3425, themerging unit 3426 and/or the playback scheduling unit 3406 may beimplemented, at least in part, as elements of a control system, e.g., bya control system of an apparatus such as that shown in FIG. 3A. Thecontrol system may include at least one of a general purpose single- ormulti-chip processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) or other programmable logic device, discrete gate or transistorlogic, or discrete hardware components. According to someimplementations, the search module 3421, the expansion unit 3425, themerging unit 3426 and/or the playback scheduling unit 3406 may beimplemented, at least in part, by one or more elements of the playbacksystem 609 shown in FIG. 6, e.g., by the playback control module 605.

In this example, the search module 3421 is capable of receiving one ormore search parameters 3422 and performing a search process according toa search index 3423, to produce a list of search results 3424. Accordingto some implementations, the search index 3423 may be comparable to thesearch index 310 that is output by the keyword spotting and indexingmodule 505 of FIG. 5. Additional examples of search indices are providedbelow. In some implementations, the search process may be a multi-stagesearch process, e.g., as described below.

In some examples, the search module 3421 may capable of performingconventional “keyword spotting” functionality, such as that as describedin D. Can and M. Saraçlar, “Lattice Indexing for Spoken Term Detection,”IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, Vol. 19,No. 8, November 2011 (“the Lattice Indexing publication”), which ishereby incorporated by reference. Alternatively, or additionally, thesearch module 3421 may capable of performing a multi-dimensional searchinvolving multiple features. Such features may include words, conferencesegments, time, conference participant emotion, endpoint location,and/or endpoint type. Various examples are provided herein.

In FIG. 34, the search module 3421 is shown receiving a list of searchparameters 3422, which may be derived from user input. In one example,if the user enters pet animal the search parameters will include pet andanimal, meaning that the user wants to find instances of the word pet orof the word animal. These and/or other search definitions and proceduresknown to those of ordinary skill in the art of search systems may beimplemented by the search module 3421. For example “san francisco” couldbe searched as a bigram if entered in quotes and may correspond to asingle entry of the parameter list 3422. Accordingly, the intersectionof the search parameters could be taken by the search module 3421instead of the union. In some implementations, the search parameters mayinclude other types of features, e.g., a search parameter indicatingthat the search should be restricted to a particular type of conferencesegment, to speech by a particular conference, to a particular date ordate range, etc.

The search index 3423 may allow high-speed matching of the searchparameters 3422 with corresponding parameters found in one or moreconference recordings. In some examples, the search index 3423 may allowthe search module 3421 to implement a finite state transducer approach,such as that described in the Lattice Indexing publication. In someimplementations, the search index 3423 may have a simpler search indexdata structure, such as that of a hash table or a binary tree. Forimplementations in which the search module 3421 implements a “keywordspotting” search, the search index 3423 may allow the user to find wordsfrom input speech recognition lattices describing the speech recognitionengine's hypotheses for each of the utterances detected in theconference. For implementations in which the search module 3421implements a multi-dimensional search as disclosed herein, the searchindex may also provide an accelerated way to find other features, suchas conference segments.

In this example, the search results 3424 may include a list ofconference excerpts hypothesized to be relevant to the searchparameters. The conference excerpts may include instances of conferenceparticipant speech that correspond with one or more words included inthe search parameters. For example, the search results 3424 may includea list of hypothesized words and an estimated word recognitionconfidence score for each hypothesized word. In some implementations,each entry on the list may include an endpoint identifier, the starttime of an excerpt (e.g., relative to a conference start time) and theend time of the excerpt. If the search index contains multipleconferences, each entry on the list may include a conference identifier.

In some implementations, the word recognition confidence score maycorrespond with a search relevance metric. However, some implementationsmay involve other types of relevance evaluation, e.g., as describedabove with reference to the conference topic determination and wordcloud generation implementations. In some embodiments the relevancemetric may be constrained to be in the range from zero to one. In otherembodiments the relevance metric may be constrained within a differentnumerical range. For example, the relevance metric may take the form ofa logarithmic cost, which may be similar to the costs C_(A) and C_(L)discussed above. In still other examples, the relevance metric may be anunconstrained quantity, which may be useful only for comparing tworesults. In some examples, the search results 3424 may be ordered indescending order of relevance. The playback scheduling unit 3406 mayschedule the most relevant results to be played back first.

In some implementations, the search system 3420 may be capable ofmodifying a start time or an end time of one or more of the instances ofconference participant speech included in the search results 3424. Inthis example, the expansion unit 3425 is capable of expanding a timeinterval corresponding to an instance of conference participant speech,thereby providing more context. For example, if the user is searchingfor the word “pet,” the expansion unit 3425 may be capable of ensuringthat some words before and/or after instances of the word “pet” areincluded in the corresponding instances of conference participantspeech. Instead of only indicating the word “pet,” the resultinginstances of conference participant speech may, for example, includecontextual words such as “I don't have many pets,” “I have a pet dognamed Leo,” etc. Therefore, a user listening to such instances ofconference participant speech may be better able to determine whichinstances are relatively more or relatively less likely to be ofinterest and may be able to decide more accurately which instances areworth listening to in more detail.

In some implementations, the expansion unit 3425 may be capable ofsubtracting a fixed offset (for example 2 seconds) from the start timeof an instance of conference participant speech, under the constraintthat the start time of the excerpt may not be earlier the start time ofthe talkspurt that contains it. In some implementations, the expansionunit 3425 may be capable of adding a fixed offset (for example 2seconds) to the end time of an instance of conference participantspeech, under the constraint that the end time of the excerpt may not belater than the end time of the talkspurt that contains it.

In this implementation, the search system 3420 includes a merging unit3426 that is capable of merging two or more instances of conferenceparticipant speech, corresponding with a single conference endpoint,that overlap in time after expansion. Accordingly, the merging unit 3426may ensure that the same instance of conference participant speech isnot heard multiple times when reviewing the search results. In someexamples, when instances of conference participant speech are merged,the merged result is assigned the highest (most relevant) of all theinput relevance scores of the merged instances.

In this example, the modified search results list produced by themerging unit 3426 forms the list of input talkspurts 3401 that is inputto the playback scheduler 3406. In some implementations, the list ofinput talkspurts 3401 may be comparable to the conference segment 1301that is described above with reference to FIG. 13.

In this implementation, the playback scheduling unit 3406 is capable ofscheduling instances of conference participant speech for playback. Insome implementations, the playback scheduling unit 3406 may be capableof scheduling an instance of conference participant speech having arelatively higher search relevance metric for playback earlier than aninstance of conference participant speech having a relatively lowersearch relevance metric.

According to some examples, the playback scheduling unit 3406 may becapable of providing functionality that is like that of the playbackscheduler 1306, which is described above with reference to FIG. 13.Similarly, the playback schedule 3411 may, in some implementations, becomparable to the output playback schedule 1311 that is described abovewith reference to FIG. 13. Accordingly, the playback scheduling unit3406 may be capable of scheduling an instance of conference participantspeech that did not previously overlap in time to be played backoverlapped in time and/or scheduling an instance of conferenceparticipant speech that was previously overlapped in time to be playedback further overlapped in time. In some instances, such scheduling maybe performed according to a set of perceptually-motivated rules, e.g.,as disclosed elsewhere herein.

FIG. 35 shows examples of playback scheduling unit, merging unit andplayback scheduling unit functionality. In this example, a searchresults portion 3501 of the search results 3424 is shown with instancesof conference participant speech 3507A-3510A arranged in input time. Theinstances are actually sorted in descending order of relevance in thisexample, as shown in the search results 3424, each instance being shownwith a corresponding search relevance metric. In this example, thesearch relevance metric values range from zero to ten. Here, theunderlying search involved a single conference recording and theendpoints 3501A and 3501B are two different example endpoints within thesame conference for which the search module 3421 has returned results.

In this implementation, the search results portion 3501 includestalkspurts 3504-3506 of the conference. In this example, the talkspurts3504 and 3506 were uttered at endpoint 3501A and the talkspurt 3505 wasuttered at endpoint 3501B.

In this example, the instance of conference participant speech 3507A isa part (e.g., one word) of the talkspurt 3504 (e.g., one sentence)uttered at the endpoint 3501A. The instance of conference participantspeech 3507A has a search relevance metric of 2. Here, the instance ofconference participant speech 3508A is a part of the talkspurt 3505uttered at the endpoint 3501B. The instance of conference participantspeech 3508A has a search relevance metric of 10. The instances ofconference participant speech 3509A and 3510A are different parts (e.g.,two different instances of a word in the sentence) of the talkspurt3506, uttered at the endpoint 3501A. The instances of conferenceparticipant speech 3509A and 3510A have search relevance metrics of 7and 8, respectively.

In this example, the search results portion 3501 also shows instances ofconference participant speech after expansion, e.g., after processing bythe expansion unit 3425 of FIG. 34. In this example, the expandedinstances of conference participant speech 3507B-3510B are shown. Thestart times and end times have been expanded, while ensuring that theresulting expanded instances of conference participant speech3507B-3510B do not extend beyond their corresponding talkspurts (forexample, the expanded instance of conference participant speech 3507Bdoes not start before the start time of the talkspurt 3504).

The block 3502 shows the modified example search results after expansionand merging, shown for clarity in input time. The instances ofconference participant speech are actually sorted in descending order ofrelevance, as shown in the modified search results list 3512. In thisexample, the instances of conference participant speech 3507C, 3508C and3510C are output from the expansion and merging processes. Here, theinstance 3507C is the same as the instance 3507B, because no merging hasoccurred after expansion. Likewise, in this example the instance 3508Cis the same as the instance 3507C, because no merging has occurred afterexpansion. However, the instances 3509B and 3510B have been mergedtogether, to form the instance 3510C. Here, the instances 3509B and3510B have been merged because these two instances of conferenceparticipant speech are from the same endpoint and overlap in time. Inthis example, the higher of the two search relevance metrics (8) isassigned to the resulting instance 3510C.

In this example, the block 3503 shows a portion of a resulting outputplayback schedule 3411 after a playback scheduling process. Because thesearch results 3511 and the modified search results 3512 are sorted indescending order of relevance, the instances of conference participantspeech 3507D, 3508D and 3510D are scheduled in output time such that thelistener hears the output in descending order of relevance. In thisexample, each of the instances of conference participant speech 3507D,3508D and 3510D are scheduled to be played back at a higher rate ofspeed than the input instances of conference participant speech 3507C,3508C and 3510C, so the corresponding time intervals have beenshortened.

Moreover, in this example overlap has been introduced between theinstances of conference participant speech 3508D and 3510D. In thisexample, the instance 3510D is scheduled to start before the instance3508D is scheduled to complete. This may be permitted according to aperceptually-motivated rule that allows such overlap for instances ofconference participant speech from different endpoints. In this example,the instance 3507D is scheduled to start when the instance 3508D isscheduled to complete, in order to eliminate the intervening timeinterval. However, the instance 3507D is not scheduled to start beforethe instance 3508D is scheduled to complete, because both instances arefrom the same endpoint.

Various implementations disclosed herein involve providing instructionsfor controlling a display to provide a graphical user interface. Somesuch methods may involve receiving input corresponding to a user'sinteraction with the graphical user interface and processing audio databased, at least in part, on the input. In some examples, the input maycorrespond to one or more parameters and/or features for performing asearch of the audio data.

According to some such implementations, the instructions for controllingthe display may include instructions for making a presentation ofconference participants. The one or more parameters and/or features forperforming the search may include an indication of a conferenceparticipant. In some examples, the instructions for controlling thedisplay may include instructions for making a presentation of conferencesegments. The one or more parameters and/or features for performing thesearch may include an indication of a conference segment. According tosome implementations, the instructions for controlling the display mayinclude instructions for making a presentation of a display area forsearch features. The one or more parameters and/or features forperforming the search may include words, time, conference participantemotion, endpoint location and/or endpoint type. Various examples aredisclosed herein.

FIG. 36 shows an example of a graphical user interface that may be usedto implement some aspects of this disclosure. In some implementations,the user interface 606 d may be presented on a display based, at leastin part, on information provided by a playback system, such as theplayback system 609 shown in FIG. 6. According to some suchimplementations, the user interface 606 d may be presented on a displayof a display device, such as the display device 610 shown in FIG. 6.

In this implementation, the user interface 606 d includes a list 2801 ofconference participants. In this example, the list 2801 of conferenceparticipants corresponds with a plurality of single-party endpoints andindicates a name and picture of each corresponding conferenceparticipant. In this example, the user interface 606 d includes awaveform display area 3601, which is showing speech waveforms 625 overtime for each of the conference participants. In this implementation,the time scale of the waveform display area 3601 is indicated by thevertical lines within the waveform display area 3601 and correspondswith the time scale of the conference recording. This time scale may bereferred to herein as “input time.”

Here, the user interface 606 d also indicates conference segments 1808Kand 1808L, which correspond to a question and answer segment and adiscussion segment, respectively. In this example, the user interface606 d also includes a play mode control 3608, which a user can togglebetween linear (input time) playback and non-linear (scheduled outputtime) playback. When playing back the scheduled output, in thisimplementation clicking the play mode control 3608 allows the user toreview a result in more detail (e.g., at a slower speed, with additionalcontext).

Here, the user interface 606 d includes transport controls 3609, whichallow the user to play, pause, rewind or fast-forward through thecontent. In this example, the user interface 606 d also includes variousquantity filters 3610, which control the number of search resultsreturned. In this example, the more dots indicated on the quantityfilter 3610, the larger number of search results that may potentially bereturned.

In this implementation, the user interface 606 d includes a searchwindow 3105 and a text field 3602 for entering search parameters. Insome examples, a user may “drag” one or more displayed features (such asa conference segment or a conference participant) into the search window3105 and/or type text in the text field 3602 in order to indicate thatthe feature(s) should be used for a search of the conference recording.In this example, block 3605 of the search window 3105 indicates that theuser has already initiated a text-based search for instances of thekeyword “Portland.”

In this example, the user interface 606 d also includes a scheduledoutput area 3604, which has a time scale in output time (which may alsobe referred to herein as “playback time”) in this example. Here, theline 3606 indicates the current playback time. Accordingly, in thisexample, the instances of conference participant speech 3604A and 3604B(which have the highest and second-highest search relevance metric,respectively) have already been played back. In this implementation, theinstances of conference participant speech 3604A and 3604B in thescheduled output area 3604 correspond with the instances of conferenceparticipant speech 3601A and 3601B shown in the waveform display area3601.

In this example, the instances of conference participant speech 3604Cand 3604D are currently being played back. Here, the instances ofconference participant speech 3604C and 3604D correspond with theinstances of conference participant speech 3601C and 3601D shown in thewaveform display area 3601. In this implementation, the instances ofconference participant speech 3604E and 3604F have not yet been playedback. In this example, the instances of conference participant speech3604E and 3604F correspond with the instances of conference participantspeech 3601E and 3601F shown in the waveform display area 3601.

In this example, the instances of conference participant speech 3604Aand 3604B, as well as the instances of conference participant speech3604C and 3604D, were scheduled to be overlapped in time duringplayback. According to some implementations, this is acceptable pursuantto a perceptually-motivated rule that indicating that two talkspurts ofa single conference participant or a single endpoint should not overlapin time, but which allows overlapped playback otherwise. However,because the instances of conference participant speech 3604E and 3604Fare from the same endpoint and the same conversational participant, theinstances of conference participant speech 3604E and 3604F have not beenscheduled for overlapped playback.

FIG. 37 shows an example of a graphical user interface being used for amulti-dimensional conference search. As in the example shown in FIG. 36,block 3605 indicates a user's selection of a conference search based, atleast in part, on a search for the keyword “Portland.” However, in thisexample the user also has dragged blocks 3705 a and 3705 b into thesearch window 3105. The block 3705 a corresponds with the conferenceparticipant Abigail Adams and the block 3705 b corresponds with a Q&Aconference segment. Accordingly, a multi-dimensional conference searchhas been performed for instances of the word “Portland” spoken byconference participant Abigail Adams during a Q&A conference segment.

In this example, the multi-dimensional conference search has returned asingle instance of conference participant speech. This instance is shownin the waveform display area 3601 as the instance of conferenceparticipant speech 3601G and is shown in the scheduled output area 3604as the instance of conference participant speech 3604G.

FIG. 38A shows an example portion of a contextually augmented speechrecognition lattice. FIGS. 38B and 38C show examples of keyword spottingindex data structures that may be generated by using a contextuallyaugmented speech recognition lattice such as that shown in FIG. 38A asinput. The examples of data structures shown for the keyword spottingindices 3860 a and 3860 b may, for example, be used to implementsearches that involve multiple conferences and/or multiple types ofcontextual information. In some implementations, the keyword spottingindex 3860 may be output by the keyword spotting and indexing module505, shown in FIG. 5, e.g., by using the results of a speech recognitionprocess (e.g., the speech recognition results 401F-405F) as input.Accordingly, the keyword spotting indices 3860 a and 3860 b may beinstances of the search index 310. In some examples, the contextuallyaugmented speech recognition lattice 3850 may be an instance of thespeech recognition results output by the automatic speech recognitionmodule 405, shown in FIG. 4. In some implementations, the contextuallyaugmented speech recognition lattice 3850 may be generated by a largevocabulary continuous speech recognition (LVCSR) process based on aweighted finite state transducer (WFST).

In FIG. 38A, times of the contextually augmented speech recognitionlattice 3850 are indicated with reference to the timeline 3801. The arcsshown in FIG. 38 link nodes or “states” of the contextually augmentedspeech recognition lattice 3850. For example, the arc 3807 c links thetwo states 3806 and 3808. The start time 3820 and end time 3822correspond with the time span 3809 of the arc 3807 c, as shown in thetimeline 3801.

In some examples, the contextually augmented speech recognition lattice3850 may include information in the format of “input:output/weight” foreach arc. In some examples, the input term may correspond with stateidentification information, as shown by the state identification data3802 for the arc 3807 b. The state identification data 3802 may be acontext-dependent Hidden Markov Model state ID in some implementations.The output term may correspond with word identification information, asshown by the word identification data 3803 for arc 3807 b. In thisexample, the “weight” term includes a word recognition confidence scoresuch as described elsewhere herein, an example of which is the score3804 for arc 3807 b.

In this example, the weight term of the contextually augmented speechrecognition lattice 3850 also includes contextual information, anexample of which is the contextual information 3805 shown for the arc3807 b. During a conference, whether an in-person conference or ateleconference, a conference participant may observe and recallcontextual information in addition to spoken words and phrases. In someexamples, the contextual information 3805 may, for example, includeaudio scene information obtained from a front-end acoustic analysis. Thecontextual information 3805 may be retrieved in different timegranularities and by various modules. Some examples are shown in thefollowing table:

TABLE 1 Contextual information Time granularity Module Endpoint typeConference System hardware Speaker Conference Speaker identificationGender Conference Gender identification Location Conference On-board GPSreceiver, IP Meeting segment Segment segmentation unit 1804 EmotionSegment analysis engine 307 Visual cues Segment Video & Screen analyzerDistance Frame Audio scene analysis Angle Frame Audio scene analysisDiffuseness Frame Audio scene analysis Signal-to-noise Frame Frontendprocessing ratio

In some implementations, not only the score 3804 but also the contextualinformation 3805 may be stored for each arc, e.g., in the form of a“tuple” containing multiple entries. A value may be assigned based onthe score and the contextual information within a corresponding timespan. In some such implementations, such data may be collected for anentire conference or for multiple conferences. These data may be inputto a statistical analysis in order to obtain a priori knowledge offactors such as context distribution. In some examples, these contextualfeatures may be normalized and clustered, and the results may be codedvia a vector quantization (VQ) process.

Two examples of data structures for a keyword spotting index 3860 areshown in FIGS. 38B and 38C. In both examples, the state identificationdata 3802/word identification data 3803 pairs for each arc of acontextually augmented speech recognition lattice have been transformedto word identification data 3803/word identification data 3803A pairsfor each arc of a corresponding keyword spotting index. FIGS. 38B and38C each show very small portions of a keyword spotting index: in theseexamples, the portions may be used to spot 3 unigrams.

In the first example, shown in FIG. 38B, the word identification data3803/word identification data 3803A pairs are included in word identityfields 3812 a-3812 c of the corresponding indexed units 3810 a-3810 c,shown in corresponding arcs 3830 a-3832 a. In this example, the score3804, the start time 3820, the end time 3822 and quantized contextualinformation (the VQ index 3825 a in this example) are stored inmulti-dimensional weight field 3813. A VQ index may sometimes bereferred to herein as a “VQ ID.” This structure, which may be referredto as a “Type I” data structure herein, has at least three potentialadvantages. First, multi-dimensional contextual information istransformed into a one-dimensional VQ index 3825 a, which can reduce theamount of storage space required for storing the keyword spotting index3860. Second, the indexing structure may be stored with both input andoutput terms in the word identity fields 3812 a-3812 c, instead of,e.g., word and position terms. This feature of the word identity fields3812 a-3812 c has the potential advantage of reducing search complexity.A third advantage is that this type of data structure (as well as the“Type 2” data structure shown in FIG. 38C) facilitates searches thatinclude recordings of multiple conferences and/or searches that mayinvolve concurrent searches for multiple types of contextualinformation.

One potential disadvantage of the Type 1 data structure is that, in someexamples, an additional post-filtering process to search words may befollowed by a process of filtering the qualified scenarios by the VQindex. In other words, a search based on a keyword spotting index 3860 ahaving a Type 1 data structure may be a two-stage process. The firststage may involve determining the desired conference(s) for searching,e.g., according to time parameters of a search query, such as start timeand end time information. The second stage may involve retrieving searchresults according to other search parameters, which may includecontext-based queries.

The Type 2 data structure shown in FIG. 38C may facilitate fastersearches. In this example, the indexed units 3811 a-3811 c includecorresponding word and VQ fields 3814 a-3814 c, which include word/VQtuples. In this example, the word and VQ fields 3814 a-3814 c include afirst word/VQ tuple that includes the word identification data 3803 anda corresponding VQ index 3825 b, as well as a second word/VQ tuple thatincludes the word identification data 3803A and a corresponding VQ index3825 c.

In this implementation, each of the indexed units 3811 a-3811 c includesa weight and time field 3815, which includes the score 3804, the starttime 3820 and the end time 3822. A keyword spotting index 3860 b havinga Type 2 data structure can provide relatively faster searches than akeyword spotting index 3860 a having a Type 1 data structure. However, akeyword spotting index 3860 b having a Type 2 data structure may requiremore storage space than a keyword spotting index 3860 a having a Type 1data structure.

FIG. 39 shows examples of clustered contextual features. This exampleshows a relationship between two salient contextual features, devicetype and location. In this example, the vertical axis indicateslocation, with outside locations corresponding to the area below the“Device” axis and inside locations corresponding to the area below theDevice axis. The Device axis indicates areas corresponding to mobiledevices, headsets, laptops and spatial capture devices (e.g., spatialconferencing telephones). In FIG. 39, the cluster 3901 corresponds withconference participants using headsets in an indoor location, whereasthe clusters 3902 and 3905 correspond with indoor and outdoor conferenceparticipants, respectively, using laptops. Here, the cluster 3903corresponds with indoor conference participants using spatialconferencing telephones, whereas the cluster 3904 corresponds withoutdoor conference participants using mobile devices.

In some implementations, time information may be removed during aprocess of contextual indexing, in part because time is a specialcontextual dimension that is sequential. Moreover, it may be challengingto build a large index, e.g., including audio data for many conferences,that includes global timestamps. As additional conferences are recordedand the corresponding audio data are processed, it may not be feasibleto rebuild the previous index using global time, because the processwould introduce additional computations for each additional conferencerecording.

FIG. 40 is a block diagram that shows an example of a hierarchical indexthat is based on time. FIG. 40 shows a hierarchical index 4000 in whicheach conference recording has a conference index 4001. There may bemultiple conference recordings in one day, and therefore multipleconference indices 4001 are indicated for a single day index 4002.Likewise, multiple day indices 4002 are indicated for a single weeklyindex 4003 and multiple weekly indices 4003 are indicated for a singlemonthly index 4004. Some implementations may include additionalhierarchical levels, e.g., yearly indices, fewer hierarchical levelsand/or different hierarchical levels.

As shown in FIG. 40, whenever a time interval for any level of thehierarchical index 4000 ends a corresponding index is built, which willbe hashed by a global timestamp hash table 4005. For example, at the endof each conference, a conference index 4001 is built in the lowest levelof the hierarchical index 4000. If, for example, during a specific daythere are three conferences, the corresponding day index 4002 may becreated by assembling the keyword spotting indices from each of thethree conferences. At the end of the week a weekly index 4003 may bemade. A monthly index 4004 may be created at the end of the month.According to some implementations, the start and end times may bemaintained by the global timestamp hash table 4005 in a hierarchy. Forexample, an upper-level timestamp hash table entry (e.g., for a weeklyindex 4003) may include a pointer to each of one or more lower-levelindices (e.g., to day indices 4002). With interrelated time contextinformation included in each layer, the hierarchical index 4000 canfacilitate fast searching across multiple conference recordings.

FIG. 41 is a block diagram that shows an example of contextual keywordsearching. In some implementations, the processes described withreference to FIG. 41 may be performed, at least on part, by a searchmodule such as the search module 3421 shown in FIG. 34 and describedabove. In this example, a received query 4101 is split into a wordcomponent 4103, a time component 4102 and a contextual component 4104.In some instances, the word component 4103 may include one or more wordsor phrases. The contextual component 4104 may include one or more typesof contextual information, including but not limited to the examplesshown in Table 1, above.

The time component 4102 may, in some examples, indicate time informationcorresponding to a single conference, whereas in other examples the timecomponent 4102 may indicate time information corresponding to multipleconferences. In this example, time information of the time component4102 is used in a process (shown as process 4105 in FIG. 41) offiltering a corresponding index via a global timestamp hash table 4005,such as that described above with reference to FIG. 40. An example ofthe process 4105 is described below with reference to FIG. 42.

In this example, a contextual index will be determined according to theinformation in the contextual component 4104. Based on the contextualindex, contextual input may be searched via a VQ codebook 4106 toretrieve a set of qualifying candidate contextual VQ IDs 4107. In someimplementations, one or more constraints, such as a distance limit (e.g.Euclidean distance), may be applied to the contextual input search.

In this example, there may be different types of contextual index unitsdepending on the keyword spotting index data structure, which may beType 1 or Type 2 data structures as shown in FIG. 38. A contextual indexunit for a Type 1 data structure may have a word-based factor transducerindex, which corresponds with the data structure of the word identityfield 3812 of a Type 1 data structure. Accordingly, a word-based factortransducer index may be used for the Type 1 context index 4109. Acontextual index unit for a Type 2 data structure may have a (word, VQID) tuple-based factor transducer index, which corresponds with the datastructure of the word and VQ field 3814 of a Type 2 data structure.Accordingly, a (word, VQ ID) tuple-based factor transducer index be usedfor the Type 2 context index 4108. In some implementations, theretrieval process may involve a Finite State Transducer compositionoperation.

FIG. 42 shows an example of a top-down timestamp-based hash search. Theexample shown in FIG. 42 may be an instance of the process 4105 that isreferenced above in the discussion of FIG. 41. In FIG. 42, each level ofthe hierarchy corresponds to a different time interval corresponding toa timestamp tuple of (St,Ed), which corresponds to a start time and anend time. Each block also includes a pointer “Pt” to one or more blocksat a different level. In this example, level 4210 is the highest levelof the hierarchy.

In this implementation, each block of level 4210 corresponds to a1-month time interval, whereas each block of level 4220 corresponds to a1-day time interval. Accordingly, it may be observed that the widths ofthe blocks in FIG. 42 do not accurately represent the corresponding timeintervals. The blocks of level 4230 correspond to individual conferencesin this example. In some such examples, the time intervals of blocks inlevel 4230 may vary according to the time interval for each conference.In this example, if a queried time interval (e.g., as indicated by thetime component 4102 of a received query 4101), does not span the entiretime interval of a higher-level block, the search will proceed to alower level to retrieve a corresponding index with more detailed timeresolution.

For instance, suppose that a received query 4101 were to include a timecomponent 4102 corresponding to conferences that occurred in the timeinterval from Oct. 1, 2014 to Nov. 2, 2014 at 2 p.m. PST. In thisexample, block 4201 corresponds to October of 2014 and block 4202corresponds to November of 2014. Therefore, the time interval of block4201 would be completely encompassed by the time interval of receivedquery 4101. However, the time interval of block 4202 would not becompletely encompassed by the time interval of the received query 4101.

Therefore, in this example a search engine (e.g., the search module3421) will extract the value to a hash key for block 4202 to obtain thepointer Pt to a lower level index, which is the level 4220 in thisimplementation. In this example, block 4203 corresponds to Nov. 1, 2104and block 4204 corresponds to Nov. 2, 2014. Therefore, the time intervalof block 4203 would be completely encompassed by the time interval ofthe received query 4101, but the time interval of block 4204 would notbe completely encompassed by the time interval of the received query4101.

Accordingly, in this example the search engine will extract the value toa hash key for block 4204 to obtain the pointer Pt to a lower levelindex, which is the level 4230 in this implementation. In this example,the time intervals of the first two conferences of Nov. 2, 2014(corresponding to blocks 4205 and 4206) are completely encompassed bythe time interval of received query 4101. In this instance, the timeinterval of the third conference of Nov. 2, 2014 (corresponding to block4207) is from 1 p.m. to 3 p.m. and would therefore not be completelyencompassed by the time interval of received query 4101. However,because the lowest level of the hierarchy corresponds to individualconferences in the example, the index corresponding to block 4207 wouldstill be utilized. Then, the entire selected index will be employed asthe index (the Type 1 context index 4109 or the Type 2 context index4108) database on which keyword spotting can be performed.

As noted above, in some implementations the retrieval process mayinvolve a Finite State Transducer composition operation. According tosome such examples, after results are obtained the weight component fromeach factor transducer arc may be retrieved (e.g., from themulti-dimensional weight field 3813 of the indexed units 3810 or fromthe weight and time field 3815 of the indexed units 3811). As shown inFIG. 41, some examples may include an additional post-filtering process4110 for Type 1 contextual indexing based retrieval to filter thequalified context via selecting results with qualified contextual IDs.When using Type 2 contextual indexing based retrieval, thepost-filtering process is not necessary and therefore the retrievalspeed may be faster.

Many of the above-described implementations that pertain to conferencesearching may be particularly useful for later review by a conferenceparticipant. Various implementations will now be described that may beparticularly useful for a person who did not participate in aconference, e.g., for a person who was unable to attend. For example, aperson reviewing a conference recording may wish to obtain a high-leveloverview of the conference to determine as quickly as possible whetherany material of interest to the listener was likely to have beendiscussed. If so, a more thorough review of the conference recording (orat least portions thereof) may be warranted. If not, no further reviewmay be needed. The listener may, for example, wish to determine whoparticipated in the conference, what topics were discussed, who did mostof the speaking, etc.

Accordingly, some implementations may involve selecting only a portionof the total conference participant speech for playback. The “portion”may include one or more instances of conference participant speech,e.g., one or more talkspurts and/or talkspurt excerpts. In someexamples, the selection process may involve a topic selection process, atalkspurt filtering process and/or an acoustic feature selectionprocess. Some examples may involve receiving an indication of a targetplayback time duration. Selecting the portion of audio data may involvemaking a time duration of the playback audio data within a thresholdtime difference of the target playback time duration. In some examples,the selection process may involve keeping only a fraction of sometalkspurts and/or removing short talkspurts, e.g., talkspurts having atime duration that is below a threshold time duration.

FIG. 43 is a flow diagram that outlines blocks of some methods ofselecting only a portion of conference participant speech for playback.The blocks of method 4300, like other methods described herein, are notnecessarily performed in the order indicated. Moreover, such methods mayinclude more or fewer blocks than shown and/or described.

In some implementations, method 4300 may be implemented, at least inpart, via instructions (e.g., software) stored on non-transitory mediasuch as those described herein, including but not limited to randomaccess memory (RAM) devices, read-only memory (ROM) devices, etc. Insome implementations, method 4300 may be implemented, at least in part,by a control system, e.g., by a control system of an apparatus such asthat shown in FIG. 3A. The control system may include at least one of ageneral purpose single- or multi-chip processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, or discrete hardware components.According to some such implementations, method 4300 may be implemented,at least in part, by one or more elements of the playback system 609shown in FIG. 6, e.g., by the playback control module 605.Alternatively, or additionally, method 4300 may be implemented, at leastin part, by one or more servers.

In this example, block 4305 involves receiving audio data correspondingto a conference recording. In this example, the audio data includes datacorresponding to conference participant speech of each of a plurality ofconference participants.

In the example shown in FIG. 43, block 4310 involves selecting only aportion of the conference participant speech as playback audio data. Insome implementations, one or more elements of the playback system 609shown in FIG. 6, such as the playback control module 605, may performthe selection process of block 4310. However, in some implementationsanother device, such as a server, may perform the selection processes ofblock 4310. According to some such implementations, the playback controlserver 650 may perform, at least in part, the selection process of block4310. In some such examples, the playback control server 650 may providethe results of the selection process to the playback system 609, e.g.,to the playback control module 605.

In this example, block 4310 involves one or more of the following: (a) atopic selection process of selecting conference participation speech forplayback according to estimated relevance of the conferenceparticipation speech to one or more conference topics; (b) a topicselection process of selecting conference participation speech forplayback according to estimated relevance of the conferenceparticipation speech to one or more topics of a conference segment; (c)removing input talkspurts having an input talkspurt time duration thatis below a threshold input talkspurt time duration; (d) a talkspurtfiltering process of removing a portion of input talkspurts having aninput talkspurt time duration that is at or above the threshold inputtalkspurt time duration; and (e) an acoustic feature selection processof selecting conference participation speech for playback according toat least one acoustic feature. As noted in various examples discussedbelow, in some implementations the selecting may involve an iterativeprocess.

A listener may wish to scan conference participant speech involving whatare estimated to be the most important conference topics. For example,some implementations that include a topic section process may involvereceiving a topic list of conference topics and determining a list ofselected conference topics. The topic list may, for example, havepreviously been generated by the topic analysis module 525, as describedabove. The list of selected conference topics may be a subset of thetopic list. Determining the list of selected conference topics mayinvolve a topic ranking process. For example, some such methods mayinvolve receiving topic ranking data indicating the estimated relevanceof each conference topic on the topic list. In some examples, the topicranking data may be based on a term frequency metric, such as the termfrequency metrics disclosed elsewhere herein. Determining the list ofselected conference topics may be based, at least in part, on the topicranking data. Some implementations may involve a topic ranking processfor each of a plurality of conference segments.

Alternatively, or additionally, some implementations may include one ormore types of talkspurt filtering processes. In some implementations, atalkspurt filtering process may involve removing an initial portion ofat least some input talkspurts. The initial portion may be a timeinterval from an input talkspurt start time to an output talkspurt starttime. In some implementations, the initial portion may be one second,two seconds, etc. Some such implementations may involve removing aninitial portion of speech near the start of long talkspurts, e.g.,talkspurts having at least a threshold time duration.

Such implementations may potentially be beneficial because people oftenstart talkspurts with “filled pauses” such as “um,” “err,” etc. Theinventors have empirically determined that if the process of selectingconference participant speech is biased to throw away the initialportion of each talkburst, the resulting digest tends to contain morerelevant content and fewer filled pauses than if the selection processkeeps speech starting at the beginning of each talkburst.

In some implementations, a talkspurt filtering process may involvecalculating an output talkspurt time duration based, at least in part,on an input talkspurt time duration. According to some suchimplementations, if it is determined that the output talkspurt timeduration exceeds an output talkspurt time threshold, the talkspurtfiltering process may involve generating multiple instances ofconference participant speech for a single input talkspurt. In someimplementations, at least one of the multiple instances of conferenceparticipant speech has an end time that corresponds with an inputtalkspurt end time. Various examples of talkspurt filtering processesare described in more detail below.

Some implementations that involve an acoustic feature selection processmay involve selecting conference participation speech for playbackaccording to pitch variance, speech rate and/or loudness. Such acousticfeatures may indicate conference participant emotion, which maycorrespond with the perceived importance of the subject matter beingdiscussed at the time of the corresponding conference participationspeech. Accordingly, selecting conference participation speech forplayback according to such acoustic features may be a useful method ofselecting noteworthy portions of conference participant speech.

As noted elsewhere herein, in some implementations the analysis engine307 may perform one of more types of analyses on the audio data todetermine conference participant mood features (See, e.g., Bachorowski,J.-A., & Owren, M. J. (2007). Vocal expressions of emotion. Lewis, M.,Haviland-Jones, J. M., & Barrett, L. F. (Eds.), Th Handbook of Emotion,3rd Edition. New York: Guilford. (in press), which is herebyincorporated by reference) such as excitement, aggression orstress/cognitive load from an audio recording. (See, e.g., Yap, TetFei., Speech production under cognitive load: Effects andclassification, Dissertation, The University of New South Wales (2012),which is hereby incorporated by reference.) In some implementations, theanalysis engine 307 may perform such analyses prior to the playbackstage. The results of one or more such analyses may be indexed, providedto the playback system 609 and used as part of a process of selectingconference participation speech for playback.

According to some implementations, method 4300 may be performed, atleast in part, according to user input. The input may, for example, bereceived in response to a user's interaction with a graphical userinterface. In some examples, the graphical user interface may beprovided on a display, such as a display of the display device 610 shownin FIG. 6, according to instructions from the playback control module605. The playback control module 605 may be capable of receiving inputcorresponding to a user's interaction with the graphical user interfaceand of processing audio data for playback based, at least in part, onthe input.

In some examples, the user input may relate to the selection process ofblock 4310. In some instances, a listener may desire to place a timelimit on the playback time of the selected conference participantspeech. For example, the listener may only have a limited time withinwhich to review the conference recording. The listener may wish to scanthe highlights of the conference recording as quickly as possible,perhaps allowing some additional time to review portions of interest.According to some such implementations, method 4300 may involvereceiving user input that includes an indication of a target playbacktime duration. The target playback time duration may, for example, be atime duration necessary to scan the conference participant speechselected and output as playback audio data in block 4310. In someexamples, the target playback time duration may not include additionaltime that a listener may require to review items of interest in detail.The user input may, for example, be received in response to a user'sinteraction with a graphical user interface.

In some such examples, the selection process of block 4310 may involveselecting conference participation speech for playback according to thetarget playback time duration. The selection process may, for example,involve making a time duration of the playback audio data within athreshold time difference of the target playback time duration. Forexample, the threshold time difference may be 10 seconds, 20 seconds, 30seconds, 40 seconds, 50 seconds, one minute, 2 minutes, 3 minutes, etc.In some implementations, the selection process may involve making a timeduration of the playback audio data within a threshold percentage of thetarget playback time duration. For example, the threshold percentage maybe 1%, 5%, 10%, etc.

In some instances, the user input may relate to one or more searchparameters. Such implementations may involve selecting conferenceparticipation speech for playback and/or scheduling instances ofconference participant speech for playback based, at least in part, on asearch relevance metric.

In this example, block 4315 involves providing the playback audio datato a speaker system (e.g., to headphones, ear buds, a speaker array,etc.) for playback. In some examples, block 4315 may involve providingthe playback audio data directly to a speaker system, whereas in otherimplementations block 4315 may involve providing the playback audio datato a device, such as the display device 610 shown in FIG. 6, which maybe capable of communication with the speaker system.

Some implementations of method 4300 may involve introducing (orchanging) overlap between instances of conference participant speech.For example, some implementations may involve scheduling an instance ofconference participant speech that did not previously overlap in timewith another instance of conference participant speech to be played backoverlapped in time and/or scheduling an instance of conferenceparticipant speech that was previously overlapped in time with anotherinstance of conference participant speech to be played back furtheroverlapped in time.

In some such implementations, the scheduling may be performed accordingto a set of perceptually-motivated rules. For example, the set ofperceptually-motivated rules may include a rule indicating that twotalkspurts of a single conference participant should not overlap in timeand/or a rule indicating that two talkspurts should not overlap in timeif the two talkspurts correspond to a single endpoint. In someimplementations, the set of perceptually-motivated rules may include arule wherein, given two consecutive input talkspurts A and B, A havingoccurred before B, the playback of an instance of conference participantspeech corresponding to B may begin before the playback of an instanceof conference participant speech corresponding to A is complete, but notbefore the playback of the instance of conference participant speechcorresponding to A has started. In some examples, the set ofperceptually-motivated rules may include a rule allowing the playback ofan instance of conference participant speech corresponding to B to beginno sooner than a time T before the playback of an instance of conferenceparticipant speech corresponding to A is complete, wherein T is greaterthan zero.

Some implementations of method 4300 may involve reducing playback timeby taking advantage of spatial rendering techniques. For example, theaudio data may include conference participant speech data from multipleendpoints, recorded separately and/or conference participant speech datafrom a single endpoint corresponding to multiple conference participantsand including spatial information for each conference participant of themultiple conference participants. Some such implementations may involverendering the playback audio data in a virtual acoustic space such thateach of the conference participants whose speech is included in theplayback audio data has a respective different virtual conferenceparticipant position.

However, in some implementations the rendering operations may be morecomplex. For example, some implementations may involve analyzing theaudio data to determine conversational dynamics data. The conversationaldynamics data may include data indicating the frequency and duration ofconference participant speech, data indicating instances of conferenceparticipant doubletalk (during which at least two conferenceparticipants are speaking simultaneously) and/or data indicatinginstances of conference participant conversations.

Some such examples may involve applying the conversational dynamics dataas one or more variables of a spatial optimization cost function of avector describing the virtual conference participant position for eachof the conference participants in the virtual acoustic space. Suchimplementations may involve applying an optimization technique to thespatial optimization cost function to determine a locally optimalsolution and assigning the virtual conference participant positions inthe virtual acoustic space based, at least in part, on the locallyoptimal solution.

Alternatively, or additionally, some implementations may involvespeeding up the played-back conference participant speech. In someimplementations, the time duration of the playback audio data isdetermined, at least in part, by multiplying a time duration of at leastsome selected portions of the conference participant speech by anacceleration coefficient. Some implementations may involve multiplyingall selected portions of the conference participant speech by anacceleration coefficient. The selected portions may correspond toindividual talkspurts, portions of talkspurts, etc. In someimplementations, the selected portions may correspond to all selectedconference participant speech of a conference segment. Some examples aredescribed below.

FIG. 44 shows an example of a selective digest module. The selectivedigest module 4400 may be capable of performing, at least in part, theoperations described above with reference to FIG. 43. In someimplementations, the selective digest module 4400 may be implemented, atleast in part, via instructions (e.g., software) stored onnon-transitory media such as those described herein, including but notlimited to random access memory (RAM) devices, read-only memory (ROM)devices, etc. In some implementations, the selective digest module 4400may be implemented, at least in part, by a control system, e.g., by acontrol system of an apparatus such as that shown in FIG. 3A. Thecontrol system may include at least one of a general purpose single- ormulti-chip processor, a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA) or other programmable logic device, discrete gate or transistorlogic, or discrete hardware components. According to some suchimplementations, the selective digest module 4400 may be implemented, atleast in part, by one or more elements of the playback system 609 shownin FIG. 6, e.g., by the playback control module 605. Alternatively, oradditionally, the selective digest module 4400 may be implemented, atleast in part, by one or more servers.

The selective digest module 4400 may, for example, be capable ofselecting only a portion of the conference participant speech containedin the received-audio data corresponding to a recording of one or moreconferences. In this example, the selective digest module 4400 iscapable of adaptively selecting instances of conference participantspeech from a received list of input talkspurts 4430A such that, whenscheduled, a time duration of the playback audio data corresponding tothe selected instances of conference participant speech will be close toa received indication of a target playback time duration 4434. Theinstances of conference participant speech may, for example, includetalkspurts and/or portions of talkspurts, the latter of which also maybe referred to herein as “talkspurt excerpts.” In some implementations,the selective digest module 4400 may be capable of making the timeduration of the playback audio data within a threshold time differenceor a threshold time percentage of the target playback time duration4434.

In some examples, the list of input talkspurts 4430A may include a listof all of the talkspurts in a conference. In alternative examples, thelist of input talkspurts 4430A may include a list of all of thetalkspurts in a particular temporal region of a conference. The temporalregion of the conference may, in some implementations, correspond with aconference segment. In some examples, the list of input talkspurts 4430Amay include, for each talkspurt, endpoint identification data, a starttime and an end time.

In the example of FIG. 44, the selective digest 4400 is shown outputtinga list of selected talkspurt excerpts 4424A. In some implementations,the list of selected talkspurt excerpts 4424A may include, for eachselected excerpt, endpoint identification data, a start time and an endtime. Various examples described herein involve outputting a list ofselected talkspurt excerpts for playback, in part because such talkspurtexcerpts may be reviewed more quickly and may, in some examples, includethe most salient portion(s) of the corresponding talkspurts. However,some implementations involve outputting a list of selected instances ofconference participant speech which may include talkspurts and/ortalkspurt excerpts.

In this example, the selective digest 4400 is also capable of schedulingthe list of selected talkspurt excerpts 4424A for playback. Accordingly,the selective digest 4400 is also shown outputting a playback schedule4411A. In this example, the playback schedule 4411A describes how toplay back a selective digest (a list of selected instances of conferenceparticipant speech) of a conference or a temporal region of ateleconference (e.g., a conference segment). The playback schedule 4411Amay, in some examples, be similar to the output playback schedule 3411shown in FIG. 34 and described above with reference to FIGS. 34 and 35.

FIG. 45 shows examples of elements of a selective digest module. In thisexample, the selective digest module 4400 includes a selector module4531 and a playback scheduling unit 4506. In this particularimplementation, the selective digest module 4400 includes an expansionunit 4525 and a merging unit 4526. However, alternative implementationsof the selective digest module 4400 may or may not include an expansionunit 4525 and/or a merging unit 4526.

Here, the selector module 4531 is shown receiving a list of inputtalkspurts 4430 and an indication of a target playback time duration4434. In this example, the selector module 4531 is capable of producinga candidate list of selected talkspurt excerpts 4424 from the list ofinput talkspurts 4430 based, at least in part, on the target playbacktime duration 4434 and a scheduled playback time duration 4533 providedby an actual duration multiplexer 4532.

In this implementation, the actual duration multiplexer 4532 determineswhether the current iteration is a first iteration and provides acorresponding scheduled playback time duration. In some implementations,the scheduled playback time duration 4533 is set to zero during thefirst iteration of the operations of the selective digest module 4400.This allows at least one iteration during which the expansion unit 4525,the merging unit 4526 and the playback scheduling unit 4506 (or, inalternative implementations that may not include an expansion unit 4525and/or a merging unit 4526, at least the playback scheduling unit 4506)may operate on excerpts of talkspurts selected by the selector module4531. In this example, during subsequent iterations the scheduledplayback time duration 4533 provided to the selector module 4531 by theactual duration multiplexer 4532 is the value of the actual scheduledplayback time duration 4535 after scheduling by the playback schedulingunit 4506. Here, the actual scheduled playback time duration 4535corresponds with the above-mentioned “time duration of the playbackaudio data.”

According to this example, when the scheduled playback time duration4533 is within a threshold range of the target playback time duration4434, the candidate list of selected talkspurt excerpts 4424 is returnedas a final list of selected talkspurt excerpts 4424A. In one suchexample, the threshold range may be +/−10%, meaning that the scheduledplayback time duration 4533 must be less than or equal to 110% of thetarget playback time duration 4434 and greater than or equal to 90% ofthe target playback time duration 4434. However, in alternative examplesthe threshold range may be a different percentage, such as 1%, 2%, 4%,5%, 8%, 12%, 15%, etc. In other implementations, the threshold range maybe a threshold time difference, such as 10 seconds, 20 seconds, 30seconds, 40 seconds, 50 seconds, one minute, 2 minutes, 3 minutes, etc.

In this example, the expansion unit 4525 is capable of modifying thestart and/or end times of the talkspurt excerpts in the candidate listof selected talkspurt excerpts 4424 to provide additional context.Accordingly, in this example the expansion unit 4525 is capable ofproviding functionality like that of the expansion unit 3425 that isdescribed above with reference to FIG. 34. Therefore, a user listeningto such instances of conference participant speech may be better able todetermine which instances are relatively more or relatively less likelyto be of interest and may be able to decide more accurately whichinstances are worth listening to in more detail. According to someimplementations, the expansion unit 4525 may be capable of subtracting afixed offset t_(ex) (for example, 1 second, 2 seconds, etc.) from thestart time of a talkspurt excerpt under the constraint that the starttime of the talkspurt excerpt may not be earlier the start time of thetalkspurt that contains it. According to some examples, the expansionunit 4525 may be capable of adding a fixed offset t_(ex) (for example, 1second, 2 seconds, etc.) to the end time of a talkspurt excerpt underthe constraint that the end time of the talkspurt excerpt may not belater than the end time of the talkspurt that contains it.

In this implementation, the merging unit 4526 is capable of merging twoor more instances of conference participant speech, corresponding with asingle conference endpoint and/or conference participant, that overlapin time after expansion. Accordingly, the merging unit 4526 may ensurethat the same instance of conference participant speech is not heardmultiple times when reviewing the search results. In this example themerging unit 4526 is capable of providing functionality like that of themerging unit 3426 that is described above with reference to FIG. 34. Thelist of modified talkspurt excerpts to schedule 4501 produced by themerging unit 4526 is asserted to the playback scheduler 4506 in thisexample.

According to some implementations, the playback scheduling unit 4506 maybe capable of providing functionality such as that of the playbackscheduler 1306, which is described above with reference to FIG. 13,and/or the playback scheduling unit 3406, which is described above withreference to FIGS. 34 and 35. Accordingly, the playback scheduling unit4506 may be capable of scheduling an instance of conference participantspeech (in this example, a modified talkspurt excerpt) that did notpreviously overlap in time with another instance of conferenceparticipant speech to be played back overlapped in time, or schedulingan instance of conference participant speech that was previouslyoverlapped in time with another instance of conference participantspeech to be played back further overlapped in time. For example, theplayback scheduling unit 4506 may be capable of scheduling modifiedtalkspurt excerpts for playback according to a set ofperceptually-motivated rules.

In this example, the playback scheduling unit 4506 is capable ofgenerating a candidate output playback schedule 4411. The candidateoutput playback schedule 4411 may, for example, be comparable to outputplayback schedule 1311 that is described above with reference to FIG. 13and/or the output playback schedule 3411 that is described above withreference to FIGS. 34 and 35. In this implementation, when the scheduledplayback time duration 4533 is within a threshold range of the targetplayback time duration 4434, the candidate output playback schedule 4411is returned as the final output playback schedule 4411A.

In the example shown in FIG. 45, the playback scheduling unit 4506returns the actual scheduled playback time duration 4535, whichcorresponds with a time for playback of the modified talkspurt excerptsafter scheduling by the playback scheduling unit 4506. In alternativeimplementations, the actual scheduled playback time duration 4535 may bedetermined outside of the playback scheduling unit 4506, e.g., bycomparing the output start time of the first entry on the candidateoutput playback schedule 4411 with the output end time of the lastentry.

FIG. 46 shows an example of a system for applying a selective digestmethod to a segmented conference. In some implementations, the selectivedigest system 4600 may be implemented, at least in part, viainstructions (e.g., software) stored on non-transitory media such asthose described herein, including but not limited to random accessmemory (RAM) devices, read-only memory (ROM) devices, etc. In someimplementations, the selective digest system 4600 may be implemented, atleast in part, by a control system, e.g., by a control system of anapparatus such as that shown in FIG. 3A. The control system may includeat least one of a general purpose single- or multi-chip processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic device, discrete gate or transistor logic, ordiscrete hardware components. According to some such implementations,the selective digest system 4600 may be implemented, at least in part,by one or more elements of the playback system 609 shown in FIG. 6,e.g., by the playback control module 605. Alternatively, oradditionally, the selective digest system 4600 may be implemented, atleast in part, by one or more servers.

In some implementations, the selective digest system 4600 may includemore or fewer elements than are shown in FIG. 46. For example, in thisimplementation the selective digest system 4600 includes a plurality ofselective digest modules 4400A-4400C, one for each conference segment.However, in some alternative implementations, audio data correspondingto some segments, such as Babble and/or Silence segments, will not beprocessed and there will be no corresponding selective digest modules4400. In this example, audio data from only three conference segments isshown being processed, but the break between the representations ofconference segments 1808B and 1808C is intended to represent one or moreadditional conference segments. Accordingly, in this example the inputaudio data 4601 represents audio data for an entire conferencerecording. Other examples may involve processing more or fewerconference segments, or processing an entire conference withoutsegmentation.

In this example, each of the selective digest modules 4400A-4400Creceives a corresponding one of the lists of input talkspurts4430A-4430C, each of which corresponds to one of the conference segments1808A-1808C. Here, each of the selective digest modules 4400A-4400Coutputs a corresponding one of the per-segment lists of selectedtalkspurt excerpts 4624A-C, one for each conference segment. Moreover,each of the selective digest modules 4400A-4400C outputs a correspondingone of the per-segment output playback schedules 4611A-4611C.Segmentation information may or may not be included in the output of theselective digest modules 4400A-4400C, depending on the particularimplementation.

In this implementation, the selective digest system 4600 includes timemultipliers 4602A-4602C, one for each conference segment for which audiodata are being processed. In some examples, the target playback time foreach segment is calculated by multiplying the input duration of eachsegment by a coefficient α, reflecting the desired factor by whichplayback is to be accelerated. In some examples, α may be in the rangefrom zero to one. Some example values of α that have successfully beenused in experimental prototypes include 0.5, 0.333, 0.25 and 0.1,corresponding to 2×, 3×, 5× and 10× speed-up in playback rate,respectively. According to some implementations, the value of α maycorrespond with user input regarding a desired speed-up in playbackrate, or a user's indication of a maximum tolerable speed-up in playbackrate.

In this example, the selective digest system 4600 includes aconcatenation unit 4603. Here, the concatenation unit 4603 is capable ofconcatenating the per-segment lists of selected talkspurt excerpts4624A-C (e.g., in order of the start times of each conference segment)into a final list of selected talkspurt excerpts 4624D. In someimplementations, the per-segment output playback schedules 4611A-4611Cmay be discarded, whereas in other implementations the per-segmentoutput playback schedules 4611A-4611C may be retained. Segmentationinformation may or may not be included in the output of theconcatenation unit 4603, depending on the particular implementation.

In this implementation, the selective digest system 4600 includes afinal playback scheduling unit 4606. In some implementations, the finalplayback scheduling unit 4606 may be capable of functionality similar tothat of the system 1700, which includes the segment scheduler unit 1710and is described above with reference to FIG. 17. Accordingly, the finalplayback scheduling unit 4606 may be capable of scheduling the selectedtalkspurt excerpts from consecutive segments to overlap in time.

In some examples, the final playback scheduling unit 4606 may be capableof functionality similar to that of the playback scheduling unit 4506,which is described above with reference to FIG. 45. In some suchexamples, the final playback scheduling unit 4606 may be capable ofscheduling the selected talkspurt excerpts of each segment to followeach other in output time. Although some talkspurt excerpts may bescheduled for overlapping playback, such implementations may not involvescheduling the selected talkspurt excerpts of entire conference segmentsfor overlapping playback.

In this example, the final playback scheduling unit 4606 outputs a finalplayback schedule 4611D, which is a schedule for all selected talkspurtexcerpts of the conference in this example. In some implementations, thefinal playback schedule 4611D corresponds with a scheduled playback timeduration that is approximately proportional to the input duration of theteleconference multiplied by the coefficient α. However, in alternativeimplementations (such as those involving simultaneous playback ofconference segments), the scheduled playback time duration may not beproportional to the input duration of the teleconference multiplied bythe coefficient α.

FIG. 47 shows examples of blocks of a selector module according to someimplementations. In this example, the selector module 4531 is capable ofproviding topic selection functionality. For example, the selectormodule 4531 may be capable of determining which instances of conferenceparticipant speech to select based on estimated relevance to the overalltopics of the conference or segment.

In this example, the selector module 4531 is shown receiving a list ofinput talkspurts 4430 and a topic list 4701. In some implementations,the list of input talkspurts 4430 and the topic list 4701 may correspondto an entire conference, whereas in other implementations the list ofinput talkspurts 4430 and the topic list 4701 may correspond to aconference segment. The topic list 4701 may, for example, correspond tothe topic list 2511 that is described above with reference to FIG. 25.In some implementations, topics in the topic list 4701 may be stored indescending order of estimated importance, e.g., according to a termfrequency metric. For each topic on the topic list 4701, there may beone or more instances of conference participant speech. Each of theinstances of conference participant speech may have an endpointindication, a start time and an end time.

In this implementation, the selector module 4531 is shown receiving atarget playback time duration 4434 and a scheduled playback timeduration 4533. The target playback time duration 4434 may be receivedaccording to user input from a user interface, e.g., as described abovewith reference to FIGS. 43 and 44. The scheduled playback time duration4533 may be received from a playback scheduling unit 4506, e.g. asdescribed above with reference to FIG. 45. In this example, the selectormodule 4531 is capable of operating in an iterative process to adjustthe number N of words to keep from the topic list 4701 until thescheduled playback time duration 4533 is within a predetermined range(e.g., a percentage or an absolute time range) of the target playbacktime duration 4434. As noted above, the term “word” as used herein mayalso include phrases, such as “living thing.” (In one example describedabove, the phrase “living thing” is described as a third-level hypernymof the word “pet,” a second-level hypernym of the word “animal” and afirst-level hypernym of the word “organism.”)

In this example, the selector module 4531 includes a top N word selector4702 that is capable of selecting the N most important words of thetopic list 4701, e.g., as estimated according to a term frequencymetric. The top N word selector 4702 may, for example, proceed throughthe topic list 4701 in descending order of estimated importance. Foreach topic encountered, the top N word selector 4702 may take words indescending order until a list 4703 of the top N words has been compiled.

In this implementation, the final value of N is determined by accordingto an iterative process performed by an adjustment module 4710, whichincludes a search adjustment unit 4705 and an N initializer 4706. Forthe first iteration, the N initializer 4706 sets N to an appropriateinitial value N₀. In this example, a state variable 4707 is shown withinadjustment module 4710, which is a variable value of N that is storedand updated from iteration to iteration.

In this example, the search adjustment unit 4705 is capable of producingan updated estimate of N based on the previous value of N and thedifference between the target playback time duration 4434 and thescheduled playback time duration 4533. If the scheduled playback timeduration 4533 is too low, the search adjustment unit 4705 may add morecontent (in other words, the value of N may be raised), whereas if thescheduled playback time duration 4533 is too high, the search adjustmentunit 4705 may remove content (in other words, the value of N may belowered).

The search adjustment unit 4705 may adjust the value of N according todifferent methods, depending on the particular implementation. In someexamples, the search adjustment unit 4705 may perform a linear search.For example, the search adjustment unit 4705 may start with N(0)=N₀=0.On each iteration, the search adjustment unit 4705 may increase N by afixed amount (e.g., by 5 or 10) until the difference between the targetplayback time duration 4434 and the scheduled playback time duration4533 is within a predetermined range.

In some implementations, the search adjustment unit 4705 may perform adifferent type of linear search. For example, the search adjustment unit4705 may start with N(0)=N₀=0. For each iteration, the search adjustmentunit 4705 may increase N such that all the words from the next topic onthe topic list 4701 are included. The search adjustment unit 4705 mayrepeat this process until the difference between the target playbacktime duration 4434 and the scheduled playback time duration 4533 iswithin a predetermined range.

In alternative implementations, the search adjustment unit 4705 mayperform a binary search. For example, during each iteration, the searchadjustment unit 4705 may maintain N_(min), a lower bound for N andN_(max), an upper bound for N. For example, the search adjustment unit4705 may start with N_(min)(0)=0, N_(max)(0)=N_(total),N(0)=N_(o)=α·N_(total), where N_(total) represents the total number ofwords included by all topics of the topic list 4701. For each iterationk, if the scheduled playback time duration 4533 is below the targetplayback time duration 4434, the search adjustment unit 4705 may setN_(min) and N_(max) as follows:

${{N_{\min}(k)} = {N\left( {k - 1} \right)}},{{N_{\max}(k)} = {N_{\max}\left( {k - 1} \right)}},{{N(k)} = {\left\lfloor \frac{{N_{\min}(k)} + {N_{\max}(k)}}{2} \right\rfloor.}}$

However, if the scheduled playback time duration 4533 is above thetarget playback time duration 4434, the search adjustment unit 4705 mayset N_(min) and N_(max) as follows:

${{N_{\min}(k)} = {N_{\min}\left( {k - 1} \right)}},{{N_{\max}(k)} = {N\left( {k - 1} \right)}},{{N(k)} = {\left\lfloor \frac{{N_{\min}(k)} + {N_{\max}(k)}}{2} \right\rfloor.}}$

The search adjustment unit 4705 may repeat this process until thedifference between the target playback time duration 4434 and thescheduled playback time duration 4533 is within a predetermined range.

After the final value of N has been determined by the adjustment module4710, the final value of N may be provided to the top N word selector4702. In this example, the top N word selector 4702 is capable ofselecting the N most important words of the topic list 4701 andoutputting the list 4703 of the top N words.

In this implementation, the list 4703 of the top N words is provided toa talkspurt filter 4704. In this example, the talkspurt filter 4704retains only excerpts of talkspurts that are present both in the list ofinput talkspurts 4430 and the list 4703 of the top N words. Retainedwords may, for example, be returned in the list of selected talkspurtexcerpts 4424 in the order they were specified in the list of inputtalkspurts 4430, e.g., in temporal order. Although not shown in FIG. 47,in some examples the list of selected talkspurt excerpts 4424 may beprocessed by an expansion unit 4525 in order to provide more context totalkspurt excerpts. In some implementations, the list of selectedtalkspurt excerpts 4424 also may be processed by a merging unit 4526.

FIGS. 48A and 48B show examples of blocks of a selector module accordingto some alternative implementations. In this example, the selectormodule 4531 is capable of providing heuristic selection functionality.For example, the selector module 4531 may be capable of removing inputtalkspurts having an input talkspurt time duration that is below athreshold input talkspurt time duration. Alternatively, or additionally,the selector module 4531 may be capable of removing a portion of atleast some input talkspurts that have an input talkspurt time durationthat is at or above the threshold input talkspurt time duration. In someimplementations, the selector module 4531 may be capable of keeping onlypart of every other talkspurt, of every third talkspurt, of every fourthtalkspurt, etc. In some implementations, the selector module 4531 may becapable of providing heuristic selection functionality withoutinformation regarding conference topics.

Some implementations of the selector module 4531 that are capable ofproviding heuristic selection functionality also may include anexpansion unit 4525. In some such implementations, when the selectormodule 4531 is providing heuristic selection functionality, the effectof the expansion unit 4525 may be limited or negated, e.g., by settingt_(ex) to zero or to a small value (e.g., 0.1 seconds, 0.2 seconds, 0.3seconds, etc.). According to some such implementations, the minimum sizeof a talkspurt excerpt may be controlled by the t_(speck) parameter thatis described below.

In this example, the selector module 4531 is shown receiving a list ofinput talkspurts 4430. In some implementations, the list of inputtalkspurts 4430 may correspond to an entire conference, whereas in otherimplementations the list of input talkspurts 4430 and the topic list4701 may correspond to a conference segment. In this implementation, theselector module 4531 is also shown receiving a target playback timeduration 4434 and a scheduled playback time duration 4533. The targetplayback time duration 4434 may be received according to user input froma user interface, e.g., as described above with reference to FIGS. 43and 44. The scheduled playback time duration 4533 may be received from aplayback scheduling unit 4506, e.g. as described above with reference toFIG. 45.

In this implementation, the selector module 4531 is capable of applyingan iterative heuristic selection process to adjust the playback time ofselected talkspurts until the scheduled playback time duration 4533 ofthe output list of selected talkspurt excerpts 4424 is within apredetermined range (e.g., a percentage or an absolute time range) ofthe target playback time duration 4434.

In this example, the selector module 4531 includes a filter 4801 and anadjustment module 4802. In some implementations, the filter 4801 mayapply two parameters, K and t_(speck). In some such implementations, Kmay represent a parameter, e.g., in the range of zero to one, whichrepresents the fraction of each talkspurt that should be kept. Accordingto some such implementations, t_(speck) may represent a time durationthreshold (e.g., a minimum time duration for a talkspurt or a talkspurtexcerpt) that may, for example, be measured in seconds.

According to some examples, for each iteration k, the adjustment module4802 may determine new values for the parameters K(k) and t_(speck)(k),based on the previous values K(k−1) and t_(speck)(k−1) and thedifference between the scheduled playback time duration 4533 and targetplayback time duration 4434. In some such examples, talkspurt excerptsthat are shorter than t_(speck) (after scaling by K) may be removed bythe filter 4801.

In some implementations, the adjustment module 4802 may apply thefollowing set of heuristic rules. On the first iteration, K may be setto a maximum value (e.g., 1) and t_(speck) may be set to zero seconds,such that all content is kept. On subsequent iterations, the value of Kmay be reduced and/or the value of t_(speck) may be increased, therebyremoving progressively more content until the difference between thescheduled playback time duration 4533 and target playback time duration4434 is within a predetermined range, e.g., according to the followingheuristic rules. First, if t_(speck) is less than a threshold (forexample, 3 seconds, 4 seconds, 5 seconds, etc.), some implementationsinvolve increasing the value of t_(speck) (for example, by 0.1 seconds,0.2 seconds or 0.3 seconds, etc., per iteration). According to some suchimplementations, short talkspurts (those below a threshold timeduration) will be removed before a process of removing portions of longtalkspurts.

If, after removing talkspurts below a threshold time duration, thedifference between the scheduled playback time duration 4533 and targetplayback time duration 4434 is still not within the predetermined range,some implementations involve reducing the value of K. In some examples,the value of K may be reduced by applying the formula K(k)=/β*K(k−1),where β is in the range (0,1) (for example, 0.8, 0.85, 0.9, 0.95, etc.).According to such examples, content will be removed until the differencebetween the scheduled playback time duration 4533 and target playbacktime duration 4434 is within the predetermined range.

According to some implementations, talkspurts from the list of inputtalkspurts 4430 may be presented to the filter 4801 in sequence, e.g.,in temporal order. As shown in FIG. 48B, for a given input talkspurt4803, having an initial time duration t₀, in some examples the filter4801 either produces a corresponding output talkspurt excerpt 4804,which is added to the list of selected talkspurt excerpts 4424, orconsumes the input talkspurt 4803 without producing a correspondingoutput talkspurt excerpt 4804.

According to some examples, the heuristic rules that govern suchoperations of the filter 4801 are as follows. In some such examples, thefilter 4801 will calculate the output time duration, t₁, of a candidateoutput talkspurt according to t₁=Kt₀. According to some such examples,if t₁<t_(speck), the filter 4801 will not produce an output talkspurt.In some examples, the filter 4801 may calculate the start time t_(s) ofthe candidate output talkspurt relative to the start time of the inputtalkspurt (4803) according to:

$\begin{matrix}{t_{s} = \left\lbrack \begin{matrix}{t_{um},{{{if}\mspace{14mu} \left( {t_{um} + t_{1}} \right)} \leq t_{0}}} \\{{t_{0} - t_{1}},{otherwise}}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 48} \right)\end{matrix}$

In Equation 48, t_(um) represents a coefficient, which may be in therange [0, 2] seconds in some examples. In some implementations, thevalue of t_(um) may be chosen such that speech near the start of longtalkspurts is generally kept, but not speech that is at the verybeginning of long talkspurts. The motivation for this choice is thatpeople often start talkspurts with filled pauses such as “um”, “err,”and the like. The inventors determined via experimentation that theresulting digest contained more relevant content and fewer filled pausesif the selector was biased to omit speech that is at the very beginningof long talkspurts (e.g., during the first 1 second of each talkspurt,during the first 1.5 seconds of each talkspurt, during the first 2seconds of each talkspurt, etc.) than if the selector module 4531 keptspeech starting at the very beginning of each talkspurt.

In some implementations, the filter 4801 may generate multiple talkspurtexcerpts for a single input talkspurt 4803. According to some suchimplementations, at least one of the multiple talkspurt excerpts mayhave an end time that corresponds with an input talkspurt end time.

In some such examples, when the time duration of a candidate outputtalkspurt t₁ exceeds a first threshold t₂ (e.g., 8 seconds, 10 seconds,12 seconds, etc.) but is less than a threshold t₃ (e.g., 15 seconds, 20seconds, 25 seconds, 30 seconds, etc.), the filter 4801 may generate twooutput talkspurt excerpts. For example, the first output talkspurtexcerpt may start at time t_(s) with respect to the start time of theinput talkspurt and may have a time duration t₁/2. In some suchexamples, the second output talkspurt excerpt also may have a timeduration t₁/2 and may start at a time that is t₁/2 before the end of theinput talkspurt 4803, such that the end time of the second outputtalkspurt excerpt corresponds with the input talkspurt's end time.

According to some such implementations, when the length of the candidateoutput talkspurt t₁ exceeds the threshold t₃, the filter 4801 maygenerate three output talkspurt excerpts. For example, the first outputtalkspurt excerpt may start at time t_(s) with respect to the start timeof the input talkspurt and may have a time duration t₁/3. The thirdoutput talkspurt excerpt may also have a time duration t₁/3 and maystart at a time that is t₁/3 before the end of the input talkspurt 4803,such that the end time of the third output talkspurt excerpt correspondswith the input talkspurt's end time. According to some such examples,the second output talkspurt excerpt also may have a time duration t₁/3and may start at time ((t₀+t_(s))−t₁/3))/2. Accordingly, the start timeof the second output talkspurt excerpt may be chosen so that secondoutput talkspurt excerpt is midway between the first and third outputtalkspurt excerpts.

In some implementations, the filter 4801 may generate four or moreoutput talkspurt excerpts. According to some such implementations, atleast one of the multiple output talkspurt excerpts may have an end timethat corresponds with an input talkspurt end time. In some suchexamples, the output talkspurt excerpts may correspond to samples takenat regular intervals from the input talkspurt 4803, so that speech oflong input talkspurts 4803 is regularly sampled.

FIG. 49 shows examples of blocks of a selector module according to otheralternative implementations. In this example, the selector module 4531is capable of providing acoustic feature selection functionality. Forexample, the selector module 4531 may be capable of determining whichinstances of conference participant speech to select based on acousticfeatures calculated for each talkspurt (such as pitch variance, speechrate, loudness, etc.), which may indicate which talkspurts arerelatively more exciting. Such functionality is based on empiricalobservations indicating that when a talker is more excited about atopic, there are corresponding acoustic features that can be used todetect such excitement. We may assume that when a talker is moreexcited, the topic may also be more interesting to the listener.

In this example, the selector module 4531 is shown receiving a list ofinput talkspurts 4430 and an acoustic feature list 4901. In someimplementations, the list of input talkspurts 4430 and the acousticfeature list 4901 may correspond to an entire conference, whereas inother implementations the list of input talkspurts 4430 and the acousticfeature list 4901 may correspond to a conference segment. For example,the analysis engine 307 may have previously performed one of more typesof analyses on the audio data of a conference recording to determineconference participant mood features such as excitement, aggression orstress/cognitive load. Some examples are described above. The acousticfeature list 4901 may be a result of such analysis. Each entry on theacoustic feature list 4901 may be an instance of conference participantspeech, such as a talkspurt or a talkspurt excerpt. Each of theinstances of conference participant speech may have an endpointindication, a start time and an end time.

In some implementations, the acoustic feature list 4901 may be stored indescending order of estimated importance, e.g., according to anexcitement metric. The excitement metric may, for example, be a functionof pitch variance, speech rate and/or loudness. However, some types of“excited speech,” such as laughter, may be easy to detect and may notnecessarily correspond to topics of importance. Instead, laughter maycorrespond to personal comments, off-topic banter, etc. Accordingly,some implementations may involve assigning a relatively low level ofimportance (e.g., by assigning a relatively lower excitement metric) todetected instances of conference participant laughter.

According to some implementations, for long talkspurts where theacoustic feature may vary greatly, the talkspurt may be split intoseveral separate entries, each ranked according to a local acousticfeature. For example, talkspurts having a time duration of more than 20seconds may be split into a series of talkspurts no more than 10 secondslong, each with separately-calculated acoustic features.

In some examples, the acoustic feature list 4901 may be based on pitchvariance. In one example, the excitement metric may be a calculated asfollows. A fundamental frequency estimate (F0) may be extracted for eachaudio frame using a known pitch tracking technique, such as the rootcepstrum technique. Then, the values of F0 may be converted tosemitones, in order to eliminate the variation between male and femaletalkers. The standard deviation of the semitone values may be calculatedfor each talkspurt or talkspurt excerpt. The standard deviation may beused as the excitement metric for that talkspurt or talkspurt excerpt.The acoustic feature list 4901 may be created by sorting the talkspurtsand/or talkspurt excerpts in descending order, according to theexcitement metric.

In this implementation, the selector module 4531 is shown receiving atarget playback time duration 4434 and a scheduled playback timeduration 4533. The target playback time duration 4434 may be receivedaccording to user input from a user interface, e.g., as described abovewith reference to FIGS. 43 and 44. The scheduled playback time duration4533 may be received from a playback scheduling unit 4506, e.g. asdescribed above with reference to FIG. 45. In this example, the selectormodule 4531 is capable of operating in an iterative process to adjustthe number N of talkspurts (or talkspurt excerpts) to keep from theacoustic feature list 4901 until the scheduled playback time duration4533 is within a predetermined range (e.g., a percentage or an absolutetime range) of the target playback time duration 4434.

In this example, the selector module 4531 includes a top N talkspurtselector 4902 that is capable of selecting the N most importanttalkspurts (or talkspurt excerpts) of the acoustic feature list 4901,e.g., as estimated according to a term frequency metric. The top Ntalkspurt selector 4902 may, for example, proceed through the acousticfeature list 4901 in descending order of estimated importance until alist 4903 of the top N talkspurts (or talkspurt excerpts) has beencompiled.

In this implementation, the final value of N is determined by accordingto an iterative process performed by an adjustment module 4910, whichincludes a search adjustment unit 4905 and an N initializer 4906. Theadjustment module 4910 may, in some implementations, be capable offunctionality such as that described above with reference to theadjustment module 4710 of FIG. 47. For the first iteration, the Ninitializer 4906 may set N to an appropriate initial value N₀. In thisexample, a state variable 4907 is shown within adjustment module 4910,which is a variable value of N that is stored and updated from iterationto iteration.

In this example, the search adjustment unit 4905 is capable of producingan updated estimate of N based on the previous value of N and thedifference between the target playback time duration 4434 and thescheduled playback time duration 4533. Generally speaking, if thescheduled playback time duration 4533 is too low, the search adjustmentunit 4905 may add more content (in other words, the value of N may beraised), whereas if the scheduled playback time duration 4533 is toohigh, the search adjustment unit 4905 may remove content (in otherwords, the value of N may be lowered).

The search adjustment unit 4905 may adjust the value of N according todifferent methods, depending on the particular implementation. In someexamples, the search adjustment unit 4905 may perform a linear search ora binary search, e.g., as described above with reference to the searchadjustment unit 4705 of FIG. 47.

After the final value of N has been determined by the adjustment module4910, the final value of N may be provided to the top N talkspurtselector 4902. In this example, the top N talkspurt selector 4902 iscapable of selecting the N most important talkspurts (or talkspurtexcerpts) of the acoustic feature list 4901 and output the list 4903 ofthe top N talkspurts (or talkspurt excerpts).

In this implementation, the list 4903 is provided to a talkspurt filter4904. In this example, the talkspurt filter 4904 retains only talkspurts(or talkspurt excerpts) that are present both in the list of inputtalkspurts 4430 and the list 4903. Retained talkspurts (or talkspurtexcerpts) may, for example, be returned in the list 4424 of selectedtalkspurts (or talkspurt excerpts), in the order they were specified inthe list of input talkspurts 4430, e.g., in temporal order. Although notshown in FIG. 49, talkspurt excerpts may be processed by an expansionunit 4525 in order to provide more context. In some implementations,talkspurt excerpts also may be processed by a merging unit 4526.

Various modifications to the implementations described in thisdisclosure may be readily apparent to those having ordinary skill in theart. The general principles defined herein may be applied to otherimplementations without departing from the scope of this disclosure. Forexample, some alternative implementations do not involve determining aterm frequency metric according to a TF-IDF algorithm. Some suchimplementations may involve using a parsimonious language model togenerate a topic list.

Some implementations may involve combining a talkspurt filtering processwith an acoustic feature selection process. According to some suchimplementations, a talkspurt filtering process that is based, at leastin part, on talkspurt time duration may be combined with an acousticfeature selection process that is based, at least in part, on pitchvariation. For example, if K were 0.5 (corresponding to an example inwhich half of an input talkspurt is retained), the half talkspurt havingthe greater pitch variation may be retained.

In another such implementation that involves combining a talkspurtfiltering process with an acoustic feature selection process, ranks forthe input talkspurts based on pitch variations and talkspurt length maybe identified and a combined rank may be generated by using a weightingfactor. In one such example, equal weight (0.5) may be assigned forpitch variation and talkspurt length. The rank threshold may be locatedat which the desired compression ratio is achieved (in other words, thethreshold at which the difference between the target playback timeduration 4434 and the scheduled playback time duration 4533 is within apredetermined range). The talkspurt that has a combined rank below thethreshold may be removed.

Alternatively, or additionally, some implementations may involvecombining a topic selection process with an acoustic feature selectionprocess. According to some such implementations, instances of conferenceparticipant speech pertaining to the same topic may be ranked accordingto an acoustic feature selection process, e.g., according to anexcitement metric such as pitch variation. In other implementations,ranks for the input talkspurts may be based on an acoustic featureselection process and a topic selection process. A combined rankingaccording to both processes may be generated by using a weightingfactor.

Some implementations may involve combining conversational dynamicsanalysis with an acoustic feature selection process. According to somesuch implementations, instances of conference participant speechcorresponding to excited responses to an utterance may be identifiedaccording to a sudden increase in an excitement metric (such as pitchvariation) and/or by a sudden increase in doubletalk after theutterance. In some examples, instances of conference participant speechcorresponding to a “stunned silence” after an utterance may beidentified by a time interval of silence after the utterance and/or by asudden increase in an excitement metric and/or by a sudden increase indoubletalk after the time interval of silence.

Thus, the claims are not intended to be limited to the implementationsshown herein, but are to be accorded the widest scope consistent withthis disclosure, the principles and the novel features disclosed herein.

What is claimed is:
 1. A method for processing audio data, the methodcomprising: receiving, by a conversational dynamics analysis module,audio data corresponding to a conference recording of a conferenceinvolving a plurality of conference participants, the audio dataincluding at least one of: (a) conference participant speech data frommultiple endpoints, recorded separately or (b) conference participantspeech data from a single endpoint corresponding to multiple conferenceparticipants and including information for identifying conferenceparticipant speech for each conference participant of the multipleconference participants; analyzing conversational dynamics of theconference recording to determine conversational dynamics data;searching the conference recording to determine instances of each of aplurality of segment classifications, each of the segmentclassifications based, at least in part, on the conversational dynamicsdata; and segmenting the conference recording into a plurality ofsegments, each of the segments corresponding with a time interval and atleast one of the segment classifications, wherein the analyzing,searching and segmenting processes are performed by the conversationaldynamics analysis module.
 2. The method of claim 1, wherein thesearching and segmenting processes are recursive.
 3. The method of claim1, wherein the searching and segmenting processes are performed multipletimes at different time scales.
 4. The method of claim 1, wherein thesearching and segmenting processes are based, at least in part, on ahierarchy of segment classifications.
 5. The method of claim 4, whereinthe hierarchy of segment classifications is based upon one or morecriteria from a list of criteria consisting of: a level of confidencewith which segments of a particular segment classification may beidentified; a level of confidence with which a start time of a segmentmay be determined; a level of confidence with which an end time of asegment may be determined; and a likelihood that a particular segmentclassification includes conference participant speech corresponding to aconference topic.
 6. The method of claim 1, wherein instances of thesegment classifications are determined according to a set of rules andwherein the rules are based on one or more conversational dynamics datatypes selected from a group of conversational dynamics data typesconsisting of: (a) a doubletalk ratio indicating a fraction of speechtime in a time interval during which at least two conferenceparticipants are speaking simultaneously; (b) a speech density metricindicating a fraction of the time interval during which there is anyconference participant speech; and (c) a dominance metric indicating afraction of total speech uttered by a dominant conference participantduring the time interval, the dominant conference participant being aconference participant who spoke the most during the time interval. 7.The method of claim 6, wherein the set of rules includes a rule thatclassifies a segment as a Mutual Silence segment if the speech densitymetric is less than a mutual silence threshold.
 8. The method of claim7, wherein the set of rules includes a rule that classifies a segment asa Babble segment if the speech density metric is greater than or equalto the mutual silence threshold and the doubletalk ratio is greater thana babble threshold.
 9. The method of claim 8, wherein the set of rulesincludes a rule that classifies a segment as a Discussion segment if thespeech density metric is greater than or equal to the silence thresholdand if the doubletalk ratio is less than or equal to the babblethreshold but greater than a discussion threshold.
 10. The method ofclaim 9, wherein the set of rules includes a rule that classifies asegment as a Presentation segment if the speech density metric isgreater than or equal to the silence threshold, if the doubletalk ratiois less than or equal to the discussion threshold and if the dominancemetric is greater than a presentation threshold.
 11. The method of claim10, wherein the set of rules includes a rule that classifies a segmentas a Question and Answer segment if the speech density metric is greaterthan or equal to the silence threshold, if the doubletalk ratio is lessthan or equal to the discussion threshold and if the dominance metric isless than or equal to the presentation threshold but greater than aquestion and answer threshold.
 12. The method of claim 11, wherein thesearching and segmenting processes are based, at least in part, on ahierarchy of segment classifications and wherein a first hierarchicallevel of the searching process involves searching the conferencerecording to determine instances of Babble segments.
 13. The method ofclaim 12, wherein a second hierarchical level of the searching processinvolves searching the conference recording to determine instances ofPresentation segments.
 14. The method of claim 13, wherein a thirdhierarchical level of the searching process involves searching theconference recording to determine instances of Question and Answersegments and wherein a fourth hierarchical level of the searchingprocess involves searching the conference recording to determineinstances of Discussion segments.
 15. The method of claim 1, whereininstances of the segment classifications are determined according to amachine learning classifier.
 16. The method of claim 15, wherein themachine learning classifier is selected from a group of machine learningclassifiers consisting of: (a) an adaptive boosting technique; (b) asupport vector machine technique; (c) a Bayesian network modeltechnique; (d) a neural networks technique; (e) a hidden Markov modeltechnique; and (f) a conditional random fields technique.
 17. Anapparatus for processing audio data, the apparatus comprising: aninterface system; and a control system capable of: receiving, via theinterface system, audio data corresponding to a conference recording ofa conference involving a plurality of conference participants, the audiodata including at least one of: (a) conference participant speech datafrom multiple endpoints, recorded separately or (b) conferenceparticipant speech data from a single endpoint corresponding to multipleconference participants and including information for identifyingconference participant speech for each conference participant of themultiple conference participants; analyzing conversational dynamics ofthe conference recording to determine conversational dynamics data;searching the conference recording to determine instances of each of aplurality of segment classifications, each of the segmentclassifications based, at least in part, on the conversational dynamicsdata; and segmenting the conference recording into a plurality ofsegments, each of the segments corresponding with a time interval and atleast one of the segment classifications.
 18. The apparatus of claim 17,wherein the control system is capable of performing the searching andsegmenting processes multiple times at different time scales.
 19. Theapparatus of claim 17, wherein the searching and segmenting processesare based, at least in part, on a hierarchy of segment classifications.20.-21. (canceled)
 22. A non-transitory medium having software storedthereon, the software including instructions for controlling one or moredevices for processing audio data, the software including instructionsfor: receiving audio data corresponding to a conference recording of aconference involving a plurality of conference participants, the audiodata including at least one of: (a) conference participant speech datafrom multiple endpoints, recorded separately or (b) conferenceparticipant speech data from a single endpoint corresponding to multipleconference participants and including information for identifyingconference participant speech for each conference participant of themultiple conference participants; analyzing conversational dynamics ofthe conference recording to determine conversational dynamics data;searching the conference recording to determine instances of each of aplurality of segment classifications, each of the segmentclassifications based, at least in part, on the conversational dynamicsdata; and segmenting the conference recording into a plurality ofsegments, each of the segments corresponding with a time interval and atleast one of the segment classifications. 23.-31. (canceled)